Archive for the ‘Software engineering’ Category.

Things to do to an algorithm

What can you do to or with an algorithm? In other words, what is a good verb to substitute for the hyphen in   “— the algorithm”?

You can learn an algorithm. Discovering classical algorithms is a large part of the Bildungsroman of a computer scientist. Sorting algorithms, graph algorithms, parsing algorithms, numerical algorithms, matrix algorithms, graphical algorithms…

You can teach an algorithm. Whether a professor or just a team leader, you explain to others why the obvious solution is not always the right one. As when  I saw that someone had implemented the traversal part of a garbage collection scheme (the “mark” of mark-and-sweep) using a recursive algorithm. Recursion is a great tool, but not here: it needs a stack of unpredictable size, and garbage collection, which you trigger when you run out of space, is not precisely the moment to start wildly allocating memory. In comes the Deutsch-Schorr-Waite algorithm, which improbably (as if tightrope-walking) subverts the structure itself to find its way forth and back.

To teach it, you can dance an algorithm. Sounds strange, but Informatics Europe gave its 2013 education award to the “AlgoRhythmics” group from at Sapientia University in Romania, which  demonstrates algorithms using central-European dances; see their rendering of Merge Sort:

(Their page has more examples. I see that recently they expanded to other kinds of dance and will let you discover binary search as flamenco and backtracking as classical ballet.) More generally you can simulate or animate an algorithm.

You can admire an algorithm. Many indeed are a source of wonder. The inner beauty of topological sort, Levenshtein or AVL can leave no one indifferent.

You can improve an algorithm. At least you can try.

You can invent an algorithm. Small or large, ambitious or mundane, but not imagined yet by anyone. Devising a new algorithm is a sort of rite of passage in our profession. If it does prove elegant, useful and elegant, you’ll get a real kick (trust me). Then you can publish the algorithm.

You can prove an algorithm, that is to say, mathematically establish its correctness. It is indeed increasingly unreasonable to publish an algorithm without correctness arguments. Maybe I have an excuse here to advertize for an an article that examines important algorithms across a wide variety of fields and showcases their main claim to correctness: their loop invariants.

You can implement an algorithm. That is much of what we do in software engineering, even if as an OO guy I would immediately add “as part of the associated data structure.

Of late, algorithms have come to be associated with yet another verb; one that I would definitely not have envisioned when first learning about algorithms in Knuth (the book) and from Knuth (the man who most certainly does not use foul language).

You can fuck an algorithm.

Thousands of British students marched recently to that slogan:

They were demonstrating against a formula (the Guardian gives the details) that decided on university admissions. The starting point for these events was a ministerial decision to select students not from their grades at exams (“A-level”), which could not take place because of Covid, but instead from their assessed performance in their schools. So far so good but the authorities decided to calibrate these results with parameters deduced from each school’s past performance. Your grade is no longer your grade: if Jill and Joan both got a B, but Jill’s school has been better at getting students into (say) Oxford in the past, then Jill’s B is worth more than Joan’s B.

The outcry was easy to predict, or should have been for a more savvy government. Students want to be judged by their own performance, not by the results of some other students they do not even know. Arguments that the sole concern was a legimitate one (an effort to compensate for possible grade inflation in some schools) ceased to be credible when it came out that on average the algorithm boosted grades from private schools by 4.7. No theoretical justification was going to be of much comfort anyway to the many students who had been admitted to the universities of their dreams on the basis of their raw grades, and after the adjustment found themselves rejected.

In the end, “Fuck the Algorithm!” worked. The government withdrew the whole scheme; it tried to lay the blame for the fiasco on the regulatory authority (Ofqual), fooling no one.

These U.K. events of August 2020 will mark a turning point in the relationship between computer science and society. Not for the revelation that our technical choices have human consequences; that is old news, even if we often pretend to ignore it. Not for the use of Information Technology as an excuse; it is as old (“Sorry, the computer does not allow that!”) as IT itself. What “Fuck the Algorithm!” highlights is the massive danger of the current rush to apply machine learning to everything.

As long as we are talking marketing campaigns (“customers who bought the product you just ordered also bought …”) or image recognition, the admiring mood remains appropriate. But now, ever more often, machine learning (usually presented as “Artificial Intelligence” to sound more impressive) gets applied to decisions affecting human lives. In the US, for example, machine-learning algorithms increasingly help judges make decisions, or make the decisions themselves. Following this slippery path is crazy and unethical. It is also dangerous, as the U.K. students’ reaction indicates.

Machine learning does what the name indicates: it reproduces and generalizes the dominant behaviors of the past. The algorithms have no notion of right and wrong; they just learn. When they affect societal issues, the potential for societal disaster is everywhere.

Amid all the enthusiasm generated by the elegant techniques invented by machine-learning pioneers over the last two decades, one barely encounters any serious reservation. Codes of ethics (from ACM and others) have little to contribute.

We should be careful, though. Either we get our act together and define exacting controls on the use of machine learning for matters affecting people’s fates, or we will see a massive rejection of algorithmic technology, the right parts along with the wrong ones.

The British students of the year 2020’s weird summer will not be the last ones to tell us to fuck the algorithm.

This article was first published in the Communications of the ACM blog.Recycled

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New master program at SIT: Webinar tomorrow

The Schaffhausen Institute of Technology (SIT) is holding a Webinar tomorrow with a set of three talks by: Serguei Beloussov, founder of Acronis and president of SIT; Michael Widenius, CTO of MariaDB and creator of MySQL Server; and Mauro Pezzè, my colleague at SIT, who will present the new master program that we have just announced, combining CS/SE topics with management and marketing courses to train future technology leaders.

The talks are in the form of a Webinar, starting at 9 AM this Tuesday (9 June). You can find all the details on the corresponding SIT page at here.

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PhD and postdoc positions in verification in Switzerland

My group, the Chair of Software Engineering, at the newly created Schaffhausen Institute of Technology has open positions for both PhD students and postdocs. We are looking for candidates with a passion for reliable software and a mix of theoretical knowledge and practical experience in software engineering. Candidates should have degrees in computer science or related fields: a doctorate for postdoc positions, a master’s degree for PhD positions. Postdoc candidates should have a substantial publication record. Experience in one or more of the following fields is a plus:

  • Software verification (axiomatic, model-checking, abstract interpretation etc.).
  • Advanced techniques of software testing.
  • Formal methods, semantics of programming languages, type theory.
  • Design by Contract, Eiffel, techniques of correctness-by-construction.
  • Cybersecurity.

 Compensation at both levels is attractive. The PhD program is conducted in cooperation with partner universities. 

 Interested candidates should send a CV and relevant documents or links to bm@sit.org. They are also welcome to contact me for details.

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Getting a program right, in nine episodes

About this article: it originated as a series of posts on the Communications of the ACM blog. I normally repost such articles here. (Even though copy-paste is usually not good, there are three reasons for this duplication: the readership seems to be largely disjoint; I can use better formatting, since their blog software is more restrictive than WordPress; and it is good to have a single repository for all my articles, including both those who originated on CACM and those who did not.) The series took the form of nine articles, where each of the first few ended with a quiz, to which the next one, published a couple of days later, provided an answer. Since all these answers are now available it would make no sense to use the same scheme, so I am instead publishing the whole thing as a single article  with nine sections, slightly adapted from the original.

I was too lazy so far to collect all the references into a single list, so numbers such as [1] refer to the list at the end of the corresponding section.


A colleague recently asked me to present a short overview of  axiomatic semantics as a guest lecture in one of his courses. I have been teaching courses on software verification for a long time (see e.g. here), so I have plenty of material; but instead of just reusing it, I decided to spend a bit of time on explaining why it is good to have a systematic approach to software verification. Here is the resulting tutorial.


 

1. Introduction and attempt #1

Say “software verification” to software professionals, or computer science students outside of a few elite departments, and most of them will think  “testing”. In a job interview, for example, show a loop-based algorithm to a programmer and ask “how would you verify it?”: most will start talking about devising clever test cases.

Far from me to berate testing [1]; in fact, I have always thought that the inevitable Dijkstra quote about testing — that it can only show the presence of errors, not their absence [2] — which everyone seems to take as an indictment and dismissal of testing (and which its author probably intended that way) is actually a fantastic advertisement for testing: a way to find bugs? Yes! Great! Where do I get it?  But that is not the same as verifying the software, which means attempting to ascertain that it has no bugs.

Until listeners realize that verification cannot just mean testing, the best course material on axiomatic semantics or other proof techniques will not attract any interest. In fact, there is somewhere a video of a talk by the great testing and public-speaking guru James Whittaker where he starts by telling his audience not to worry, this won’t be a standard boring lecture, he will not start talking about loop invariants [3]! (Loop invariants are coming in this article, in fact they are one of its central concepts, but in later sections only, so don’t bring the sleeping bags yet.) I decided to start my lecture by giving an example of what happens when you do not use proper verification. More than one example, in fact, as you will see.

A warning about this article: there is nothing new here. I am using an example from my 1990 book Introduction to the Theory of Programming Languages (exercise 9.12). Going even further back, a 1983 “Programming Pearls” Communications of the ACM article by Jon Bentley [4] addresses the same example with the same basic ideas. Yet almost forty years later these ideas are still not widely known among practitioners. So consider these articles as yet another tutorial on fundamental software engineering stuff.

The tutorial is a quiz. We start with a program text:

from

i := 1 ; j := n              — Result initialized to 0.

until i = j loop

m := (i + j) // 2         — Integer division

if t [m] ≤ x then i := m  else  j := m end

end

if x = t [i] then Result := i end

All variables are of integer type. t is an up-sorted array of integers, indexed from 1 to n . We do not let any notation get between friends. A loop from p until e loop q end executes p then, repeatedly: stops if e (the exit condition) is true, otherwise executes q. (Like {p ; while not e do {q}} in some other notations.) “:=” is assignment, “=” equality testing.  “//” is integer division, e.g. 6 //3 = 7 //3 = 2. Result is the name of a special variable whose final value will be returned by this computation (as part of a function, but we only look at the body). Result is automatically initialized to zero like all integer variables, so if execution does not assign anything to Result the function will return zero.

First question: what is this program trying to do?

OK, this is not the real quiz. I assume you know the answer: it is an attempt at “binary search”, which finds an element in the array, or determines its absence, in a sequence of about log2 (n) steps, rather than n if we were use sequential search.  (Remember we assume the array is sorted.) Result should give us a position where x appears in the array, if it does, and otherwise be zero.

Now for the real quiz: does this program meet this goal?

The answer should be either yes or no. (If no, I am not asking for a correct version, at least not yet, and in any case you can find some in the literature.) The situation is very non-symmetric, we might say Popperian:

  • To justify a no answer it suffices of a single example, a particular array t and a particular value x, for which the program fails to set Result as it should.
  • To justify a yes answer we need to provide a credible argument that for every t and  x the program sets Result as it should.

Notes to section 1

[1] The TAP conference series (Tests And Proofs), which Yuri Gurevich and I started, explores the complementarity between the two approaches.

[2] Dijkstra first published his observation in 1969. He did not need consider the case of infinite input sets: even for a trivial finite program that multiplies two 32-bit integers, the number of cases to be examined, 264, is beyond human reach. More so today with 64-bit integers. Looking at this from a 2020 perspective, we may note that exhaustive testing of a finite set of cases, which Dijkstra dismissed as impossible in practice, is in fact exactly what the respected model checking verification technique does; not on the original program, but on a simplified — abstracted — version precisely designed to keep the number of cases tractable. Dijkstra’s argument remains valid, of course, for  the original program if non-trivial. And model-checking does not get us out of the woods: while we are safe if its “testing” finds no bug, if it does find one we have to ensure that the bug is a property of the original program rather than an artifact of the abstraction process.

[3] It is somewhere on YouTube, although I cannot find it right now.

[4] Jon Bentley: Programming Pearls: Writing Correct Programs, in Communications of the ACM, vol. 26, no. 12, pp. 1040-1045, December 1983, available for example here.


2. Attempt #2

Was program #1 correct? If so it should yield the correct answer. (An answer is correct if either Result is the index in t of an element equal to x, or Result = 0 and x does not appear in t.)

This program is not correct. To prove that it is not correct it suffices of a single example (test case) for which the program does not  “yield the correct answer”. Assume x = 1 and the array t has two elements both equal to zero (n = 2, remember that arrays are indexed from 1):

t = [0   0]

The successive values of the variables and expressions are:

                                            m       i          j            i + j + 1

After initialization:                   1         2                3

i ≠ j, so enter loop:           1       1        2                 6         — First branch of “if” since t [1] ≤ x
— so i gets assigned the value of m

But then neither of the values of i and j has changed, so the loop will repeat its body identically (taking the first branch) forever. It is not even that the program yields an incorrect answer: it does not yield an answer at all!

Note (in reference to the famous Dijkstra quote mentioned in the first article), that while it is common to pit tests against proofs, a test can actually be a proof: a test that fails is a proof that the program is incorrect. As valid as the most complex mathematical proof. It may not be the kind of proof we like most (our customers tend to prefer a guarantee that the program is correct), but it is a proof all right.

We are now ready for the second attempt:

—  Program attempt #2.

from

i := 1 ; j := n

until i = j or Result > 0  loop

m := (i + j) // 2         — Integer division

if t [m] ≤ x then

i := m  + 1

elseif t [m] = x then

Result := m

else                         — In this case t [m] > x

j := m – 1

end

end

Unlike the previous one this version always changes i or j, so we may hope it does not loop forever. It has a nice symmetry between i and j.

Same question as before: does this program meet its goal?


3. Attempt #3

The question about program #2, as about program #1: was: it right?

Again no.  A trivial example disproves it: n = 1, the array t contains a single element t [1] = 0, x = 0. Then the initialization sets both i and j to 1, i = j holds on entry to the loop which stops immediately, but Result is zero whereas it should be 1 (the place where x appears).

Here now is attempt #3, let us see it if fares better:

—  Program attempt #3.

from

i := 1 ; j := n

until i = j loop

m := (i + j + 1) // 2

if t [m] ≤ x then

i := m  + 1

else

j := m

end

end

if 1  ≤ i  and i ≤ n then Result := i end
       — If not, Result remains 0.

What about this one?


3. Attempt #4 (also includes 3′)

The first two program attempts were wrong. What about the third?

I know, you have every right to be upset at me, but the answer is no once more.

Consider a two-element array t = [0 0] (so n = 2, remember that our arrays are indexed from 1 by convention) and a search value x = 1. The successive values of the variables and expressions are:

                                                  m          i          j            i + j + 1

After initialization:                            1        2           4

i ≠ j, so enter loop:               2           3        2          6                  — First branch of “if” since t [2] < x

i ≠ j,  enter loop again:        3           ⚠                                       — Out-of-bounds memory access!
— (trying to access non-existent t [3])

Oops!

Note that we could hope to get rid of the array overflow by initializing i to 0 rather than 1. This variant (version #3′) is left as a bonus question to the patient reader. (Hint: it is also not correct. Find a counter-example.)

OK, this has to end at some point. What about the following version (#4): is it right?

—  Program attempt #4.

from

i := 0 ; j := n + 1

until i = j loop

m := (i + j) // 2

if t [m] ≤ x then

i := m  + 1

else

j := m

end

end

if 1 ≤ i  and i ≤ n then Result := i end


5. Attempt #5

Yes, I know, this is dragging on. But that’s part of the idea: witnessing how hard it is to get a program right if you just judging by the seat of your pants. Maybe we can get it right this time?

Are we there yet? Is program attempt #4 finally correct?

Sorry to disappoint, but no. Consider a two-element array t = [0 0], so n = 2, and a search value x = 1 (yes, same counter-example as last time, although here we could also use x = 0). The successive values of the variables and expressions are:

                                                 m          i          j            i + j

After initialization:                           0        3           3

i ≠ j, so enter loop:               1           2       3          5            — First branch of “if

i ≠ j, enter loop again:         2         3        3         6            — First branch again

i = j, exit loop

The condition of the final “if” is true, so Result gets the value 3. This is quite wrong, since there is no element at position 3, and in any case x does not appear in t.

But we are so close! Something like this should work, should it not?

So patience, patience, let us tweak it just one trifle more, OK?

—  Program attempt #5.

from

i := 0 ; j := n

until i ≥ j or Result > 0 loop

m := (i + j) // 2

if t [m] < x then

i := m + 1

elseif  t [m] > x then

j := m

else

Result := m

end

end

Does it work now?


6. Attempt #6

The question about program #5  was the same as before: is it right, is it wrong?

Well, I know you are growing more upset at me with each section, but the answer is still that this program is wrong. But the way it is wrong is somewhat specific; and it applies, in fact, to all previous variants as well.

This particular wrongness (fancy word for “bug”) has a history. As I pointed out in the first article, there is a long tradition of using binary search to illustrate software correctness issues. A number of versions were published and proved correct, including one in the justly admired Programming Pearls series by Jon Bentley. Then in 2006 Joshua Bloch, then at Google, published a now legendary blog article [2] which showed that all these versions suffered from a major flaw: to obtain m, the approximate mid-point between i and j, they compute

(i + j) // 2

which, working on computer integers rather than mathematical integers, might overflow! This in a situation in which both i and j, and hence m as well, are well within the range of the computer’s representable integers, 2-n to 2n (give or take 1) where n is typically 31 or, these days, 63, so that there is no conceptual justification for the overflow.

In the specification that I have used for this article, i starts at 1, so the problem will only arise for an array that occupies half of the memory or more, which is a rather extreme case (but still should be handled properly). In the general case, it is often useful to use arrays with arbitrary bounds (as in Eiffel), so we can have even a small array, with high indices, for which the computation will produce an overflow and bad results.

The Bloch gotcha is a stark reminder that in considering the correctness of programs we must include all relevant aspects and consider programs as they are executed on a real computer, not as we wish they were executed in an ideal model world.

(Note that Jon Bentley alluded to this requirement in his original article: while he did not explicitly mention integer overflow, he felt it necessary to complement his proof by the comment that that  “As laborious as our proof of binary search was, it is still unfinished by some standards. How would you prove that the program is free of runtime errors (such as division by zero, word overflow, or array indices out of bounds)?” Prescient words!)

It is easy to correct the potential arithmetic overflow bug: instead of (i + j) // 2, Bloch suggested we compute the average as

i + (j – i) // 2

which is the same from a mathematician’s viewpoint, and indeed will compute the same value if both variants compute one, but will not overflow if both i and j are within range.

So we are ready for version 6, which is the same as version 5 save for that single change:

—  Program attempt #6.

from

i := 0 ; j := n

until i ≥ j or Result > 0 loop

m := i + (j – i) // 2

if t [m] < x then

i := m + 1

elseif  t [m] > x then

j := m

else

Result := m

end

end

Now is probably the right time to recall the words by which Donald Knuth introduces binary search in the original 1973 tome on Sorting and Searching of his seminal book series The Art of Computer Programming:knuth

Although the basic idea of binary search is comparatively straightforward, the details can be somewhat tricky, and many good programmers have done it wrong the first few times they tried.

Do you need more convincing? Be careful what you answer, I have more variants up my sleeve and can come up with many more almost-right-but-actually-wrong program attempts if you nudge me. But OK, even the best things have an end. This is not the last section yet, but that was the last program attempt. To the naturally following next question in this running quiz,  “is version 6 right or wrong”, I can provide the answer: it is, to the best of my knowledge, a correct program. Yes! [3].

But the quiz continues. Since answers to the previous questions were all  that the programs were not correct, it sufficed in each case to find one case for which the program did not behave as expected. Our next question is of a different nature: can you find an argument why version #6 is correct?

References for section 6

[1] (In particular) Jon Bentley: Programming Pearls — Writing Correct Programs, in Communications of the ACM, vol. 26, no. 12, December 1983, pages 1040-1045, available here.

[2] Joshua Bloch: Extra, Extra — Read All About It: Nearly All Binary Searches and Mergesorts are Broken, blog post, on the Google AI Blog, 2 June 2006, available here.

[3] A caveat: the program is correct barring any typos or copy-paste errors — I am starting from rigorously verified programs (see the next posts), but the blogging system’s UI and text processing facilities are not the best possible for entering precise technical text such as code. However carefully I check, I cannot rule out a clerical mistake, which of course would be corrected as soon as it is identified.


7. Using a program prover

Preceding sections presented candidate binary search algorithms and asked whether they are correct. “Correct” means something quite precise: that for an array t and a value x, the final value of the variable Result is a valid index of t (that is to say, is between 1 and n, the size of t) if and only if x appears at that index in t.

The last section boldly stated that program attempt #6 was correct. The question was: why?

In the case of the preceding versions, which were incorrect, you could prove that property, and I do mean prove, simply by exhibiting a single counter-example: a single t and x for which the program does not correctly set Result. Now that I asserting the program to be correct, one example, or a million examples, do not suffice. In fact they are almost irrelevant. Test as much as you like and get correct results every time, you cannot get rid of the gnawing fear that if you had just tested one more time after the millionth test you would have produced a failure. Since the set of possible tests is infinite there is no solution in sight [1].

We need a proof.

I am going to explain that proof in the next section, but before that I would like to give you an opportunity to look at the proof by yourself. I wrote in one of the earlier articles that most of what I have to say was already present in Jon Bentley’s 1983 Programming Pearls contribution [2], but a dramatic change did occur in the four decades since: the appearance of automated proof system that can handle significant, realistic programs. One such system, AutoProof, was developed at the Chair of Software engineering at ETH Zurich [3] (key project members were Carlo Furia, Martin Nordio, Nadia Polikarpova and Julian Tschannen, with initial contributions by Bernd Schoeller) on the basis of the Boogie proof technology from Microsoft Research).

AutoProof is available for online use, and it turns out that one of the basic tutorial examples is binary search. You can go to the corresponding page and run the proof.

I am going to let you try this out (and, if you are curious, other online AutoProof examples as well) without too many explanations; those will come in the next section. Let me simply name the basic proof technique: loop invariant. A loop invariant is a property INV associated with a loop, such that:

  • A. After the loop’s initialization, INV will hold.
  • B. One execution of the loop’s body, if started with INV satisfied (and the loop’s exit condition not satisfied, otherwise we wouldn’t be executing the body!), satisfies INV again when it terminates.

This idea is of course the same as that of a proof by induction in mathematics: the initialization corresponds to the base step (proving that P (0) holds) and the body property to the induction step (proving that from P (n) follows P (n + 1). With a traditional induction proof we deduce that the property (P (n)) holds for all integers. For the loop, we deduce that when the loop finishes its execution:

  • The invariant still holds, since executing the loop means executing the initialization once then the loop body zero or more times.
  • And of course the exit condition also holds, since otherwise we would still be looping.

That is how we prove the correctness of a loop: the conjunction of the invariant and the exit condition must yield the property that we seek (in the example, the property, stated above of Result relative to t and x).

We also need to prove that the loop does terminate. This part involves another concept, the loop’s variant, which I will explain in the next section.

For the moment I will not say anything more and let you look at the AutoProof example page (again, you will find it here), run the verification, and read the invariant and other formal elements in the code.

To “run the verification” just click the Verify button on the page. Let me emphasize (and emphasize again and again and again) that clicking Verify will not run the code. There is no execution engine in AutoProof, and the verification does not use any test cases. It processes the text of the program as it appears on the page and below. It applies mathematical techniques to perform the proof; the core property to be proved is that the proposed loop invariant is indeed invariant (i.e. satisfies properties A and B above).

The program being proved on the AutoProof example page is version #6 from the last section, with different variable names. So far for brevity I have used short names such as i, j and m but the program on the AutoProof site applies good naming practices with variables called low, up, middle and the like. So here is that version again with the new variable names:

—  Program attempt #7  (identical to #6 with different variable names) .

from

low := 0 ; up := n

until low ≥ up or Result > 0 loop

middle := low + ((up – low) // 2)

if a [middle] < value then      — The array is now called a rather than t

low := middle + 1

elseif  a [middle] > value then

up := middle

else

Result := middle

end

end

This is exactly the algorithm text on the AutoProof page, the one that you are invited to let AutoProof verify for you. I wrote “algorithm text” rather than “program text” because the actual program text (in Eiffel) includes variant and invariant clauses which do not affect the program’s execution but make the proof possible.

Whether or not these concepts (invariant, variant, program proof) are completely new to you, do try the prover and take a look at the proof-supporting clauses. In the next article I will remove any remaining mystery.

Note and references for section 7

[1] Technically the set of possible [array, value] pairs is finite, but of a size defying human abilities. As I pointed out in the first section, the “model checking” and “abstract interpretation” verification techniques actually attempt to perform an exhaustive test anyway, after drastically reducing the size of the search space. That will be for some other article.

[2]  Jon Bentley: Programming Pearls: Writing Correct Programs, in Communications of the ACM, vol. 26, no. 12, pp. 1040-1045, December 1983, available for example here.

[3] The AutoProof page contains documentations and numerous article references.


8. Understanding the proof

The previous section invited you to run the verification on the AutoProof tutorial page dedicated to the example. AutoProof is an automated proof system for programs. This is just a matter of clicking  “Verify”, but more importantly, you should read the annotations added to the program text, particularly the loop invariant, which make the verification possible. (To avoid any confusion let me emphasize once more that clicking “Verify” does not run the program, and that no test cases are used; the effect is to run the verifier, which attempts to prove the correctness of the program by working solely on the program text.)

Here is the program text again, reverting for brevity to the shorter identifiers (the version on the AutoProof page has more expressive ones):

from

i := 0 ; j := n

until i ≥ j or Result > 0 loop

m := i + (j – i) // 2

if t [m] < x then

i := m + 1

elseif  t [m] > x then

j := m

else

Result := m

end

end

Let us now see what makes the proof possible. The key property is the loop invariant, which reads

A:   1  ≤ i  ≤ j  ≤ n + 1
B:   0  ≤ Result  ≤ n
C:   ∀ k: 1 .. i –1  |  t [k] < x
D:   ∀ k: j .. n  |  t [k] > x
E:    (Result > 0)   ⇒   (t [Result] = x)

The notation is slightly different on the Web page to adapt to the Eiffel language as it existed at the time it was produced; in today’s Eiffel you can write the invariant almost as shown above. Long live Unicode, allowing us to use symbols such as (obtained not by typing them but by using smart completion, e.g. you start typing “forall” and you can select the symbol that pops up), for  “implies” and many others

Remember that the invariant has to be established by the loop’s initialization and preserved by every iteration. The role of each of its clauses is as follows:

  • A: keep the indices in range.
  • B: keep the variable Result, whose final value will be returned by the function, in range.
  • C and D: eliminate index intervals in which we have determined that the sought value, x, does not appear. Before i, array values are smaller; starting at j, they are greater. So these two intervals, 1..i and j..n, cannot contain the sought value. The overall idea of the algorithm (and most other search algorithms) is to extend one of these two intervals, so as to narrow down the remaining part of 1..n where x may appear.
  • E: express that as soon as we find a positive (non-zero) Result, its value is an index in the array (see B) where x does appear.

Why is this invariant useful? The answer is that on exit it gives us what we want from the algorithm. The exit condition, recalled above, is

i ≥ j or Result > 0

Combined with the invariant, it tells us that on exit one of the following will hold:

  • Result > 0, but then because of E we know that x appears at position Result.
  • i < j, but then A,  C and D  imply that x does not appear anywhere in t. In that case it cannot be true that Result > 0, but then because of B Result must be zero.

What AutoProof proves, mechanically, is that under the function’s precondition (that the array is sorted):

  • The initialization ensures the invariant.
  • The loop body, assuming that the invariant is satisfied but the exit condition is not, ensures the loop invariant again after it executes.
  • The combination of the invariant and the exit condition ensures, as just explained, the postcondition of the function (the property that Result will either be positive and the index of an element equal to x, or zero with the guarantee that x appears nowhere in t).

Such a proof guarantees the correctness of the program if it terminates. We (and AutoProof) must prove separately that it does terminate. The technique is simple: find a “loop variant”, an integer quantity v  which remains non-negative throughout the loop (in other words, the loop invariant includes or implies v ≥ 0) and decreases on each iteration, so that the loop cannot continue executing forever. An obvious variant here is j – i + 1 (where the + 1 is needed because j – i may go down to -1 on the last iteration if x does not appear in the array). It reflects the informal idea of the algorithm: repeatedly decrease an interval i .. j – 1 (initially, 1 .. n) guaranteed to be such that x appears in t if and only if it appears at an index in that interval. At the end, either we already found x or the interval is empty, implying that x does not appear at all.

A great reference on variants and the techniques for proving program termination is a Communications of the ACM article of 2011: [3].

The variant gives an upper bound on the number of iterations that remain at any time. In sequential search, j – i + 1 would be our best bet; but for binary search it is easy to show that  log(j – i + 1) is also a variant, extending the proof of correctness with a proof of performance (the key goal of binary search being to ensure a logarithmic rather than linear execution time).

This example is, I hope, enough to highlight the crucial role of loop invariants and loop variants in reasoning about loops. How did we get the invariant? It looks like I pulled it out of a hat. But in fact if we go the other way round (as advocated in classic books [1] [2]) and develop the invariant and the loop together the process unfolds itself naturally and there is nothing mysterious about the invariant.

Here I cannot resist quoting (thirty years on!) from my own book Introduction to the Theory of Programming Languages [4]. It has a chapter on axiomatic semantics (also known as Hoare logic, the basis for the ideas used in this discussion), which I just made available: see here [5]. Its exercise 9.12 is the starting point for this series of articles. Here is how the book explains how to design the program and the invariant [6]:

In the general case [of search, binary or not] we aim for a loop body of the form

m := ‘‘Some value in 1.. n such that i ≤ m < j’’;

if t [m] ≤ x then

i := m + 1

else

j := m

end

It is essential to get all the details right (and easy to get some wrong):

  • The instruction must always decrease the variant j – i, by increasing i or decreasing j. If the the definition of m specified just m ≤ j rather than m < j, the second branch would not meet this goal.
  •  This does not transpose directly to i: requiring i < m < j would lead to an impossibility when j – i is equal to 1. So we accept i ≤ m but then we must take m + 1, not m, as the new value of i in the first branch.
  •  The conditional’s guards are tests on t [m], so m must always be in the interval 1 . . n. This follows from the clause 0 ≤ i ≤ j ≤ n + 1 which is part of the invariant.
  •  If this clause is satisfied, then m ≤ n and m > 0, so the conditional instruction indeed leaves this clause invariant.
  • You are invited to check that both branches of the conditional also preserve the rest of the invariant.
  • Any policy for choosing m is acceptable if it conforms to the above scheme. Two simple choices are i  and j – 1; they lead to variants of the sequential search algorithm [which the book discussed just before binary search].

For binary search, m will be roughly equal to the average of i and j.

“Roughly” because we need an integer, hence the // (integer division).

In the last section, I will reflect further on the lessons we can draw from this example, and the practical significance of the key concept of invariant.

References and notes for section 8

[1] E.W. Dijkstra: A Discipline of Programming, Prentice Hall, 1976.

[2] David Gries: The Science of Programming, Springer, 1989.

[3] Byron Cook, Andreas  Podelski and Andrey Rybalchenko: Proving program termination, in Communications of the ACM, vol. 54, no. 11, May 2011, pages 88-98, available here.

[4] Bertrand Meyer, Introduction to the Theory of Programming Languages, Prentice Hall, 1990. The book is out of print but can be found used, e.g. on Amazon. See the next entry for an electronic version of two chapters.

[5] Bertrand Meyer Axiomatic semantics, chapter 9 from [3], available here. Note that the PDF was reconstructed from an old text-processing system (troff); the figures could not be recreated and are missing. (One of these days I might have the patience of scanning them from a book copy and adding them. Unless someone wants to help.) I also put online, with the same caveat, chapter 2 on notations and mathematical basis: see here.

[6] Page 383 of [4] and [5]. The text is verbatim except a slight adaptation of the programming notation and a replacement of the variables: i in the book corresponds to i – 1 here, and j to j – 1. As a matter of fact I prefer the original conventions from the book (purely as a matter of taste, since the two are rigorously equivalent), but I changed here to the conventions of the program as it appears in the AutoProof page, with the obvious advantage that you can verify it mechanically. The text extract is otherwise exactly as in the 1990 book.

9. Lessons learned

What was this journey about?

We started with a succession of attempts that might have “felt right” but were in fact all wrong, each in its own way: giving the wrong answer in some cases, crashing (by trying to access an array outside of its index interval) in some cases, looping forever in some cases. Always “in some cases”,  evidencing the limits of testing, which can never guarantee that it exercises all the problem cases. A correct program is one that works in all cases. The final version was correct; you were able to prove its correctness with an online tool and then to understand (I hope) what lies behind that proof.

To show how to prove such correctness properties, I have referred throughout the series to publications from the 1990s (my own Introduction to The Theory of Programming Languages), the 1980s (Jon Bentley’s Programming Pearls columns, Gries’s Science of Programming), and even the 1970s (Dijkstra’s Discipline of Programming). I noted that the essence of my argument appeared in a different form in one of Bentley’s Communications articles. What is the same and what has changed?

The core concepts have been known for a long time and remain applicable: assertion, invariant, variant and a few others, although they are much better understood today thanks to decades of theoretical work to solidify the foundation. Termination also has a more satisfactory theory.

On the practical side, however, the progress has been momentous. Considerable engineering has gone into making sure that the techniques scaled up. At the time of Bentley’s article, binary search was typical of the kind of programs that could be proved correct, and the proof had to proceed manually. Today, we can tackle much bigger programs, and use tools to perform the verification.

Choosing binary search again as an example today has the obvious advantage that everyone can understand all the details, but should not be construed as representative of the state of the art. Today’s proof systems are far more sophisticated. Entire operating systems, for example, have been mechanically (that is to say, through a software tool) proved correct. In the AutoProof case, a major achievement was the proof of correctness [1] of an entire data structure (collections) library, EiffelBase 2. In that case, the challenge was not so much size (about 8,000 source lines of code), but the complexity of both:

  • The scope of the verification, involving the full range of mechanisms of a modern object-oriented programming language, with classes,  inheritance (single and multiple), polymorphism, dynamic binding, generics, exception handling etc.
  • The code itself, using sophisticated data structures and algorithms, involving in particular advanced pointer manipulations.

In both cases, progress has required advances on both the science and engineering sides. For example, the early work on program verification assumed a bare-bones programming language, with assignments, conditionals, loops, routines, and not much more. But real programs use many other constructs, growing ever richer as programming languages develop. To cover exception handling in AutoProof required both theoretical modeling of this construct (which appeared in [2]) and implementation work.

More generally, scaling up verification capabilities from the small examples of 30 years ago to the sophisticated software that can be verified today required the considerable effort of an entire community. AutoProof, for example, sits at the top of a tool stack relying on the Boogie environment from Microsoft Research, itself relying on the Z3 theorem prover. Many person-decades of work make the result possible.

tool_stack

Beyond the tools, the concepts are esssential. One of them, loop invariants, has been illustrated in the final version of our program. I noted in the first article the example of a well-known expert and speaker on testing who found no better way to announce that a video would not be boring than  “relax, we are not going to talk about loop invariants.” Funny perhaps, but unfair. Loop invariants are one of the most beautiful concepts of computer science. Not so surprisingly, because loop invariants are the application to programming of the concept of mathematical induction. According to the great mathematician Henri Poincaré, all of mathematics rests on induction; maybe he exaggerated, maybe not, but who would think of teaching mathematics without explaining induction? Teaching programming without explaining loop invariants is no better.

Below is an illustration (if you will accept my psychedelic diagram) of what a loop is about, as a problem-solving technique. Sometimes we can get the solution directly. Sometimes we identify several steps to the solution; then we use a sequence (A ; B; C). Sometimes we can find two (or more) different ways of solving the problem in different cases; then we use a conditional (if c then A else B end). And sometimes we can only get a solution by getting closer repeatedly, not necessarily knowing in advance how many times we will have to advance towards it; then, we use a loop.

loop_strategy

We identify an often large (i.e. very general) area where we know the solution will lie; we call that area the loop invariant. The solution or solutions (there may be more than one) will have to satisfy a certain condition; we call it the exit condition. From wherever we are, we shoot into the invariant region, using an appropriate operation; we call it the initialization. Then we execute as many times as needed (maybe zero if our first shot was lucky) an operation that gets us closer to that goal; we call it the loop body. To guarantee termination, we must have some kind of upper bound of the distance to the goal, decreasing each time discretely; we call it the loop variant.

This explanation is only an illustration, but I hope it makes the ideas intuitive. The key to a loop is its invariant. As the figure suggests, the invariant is always a generalization of the goal. For example, in binary search (and many other search algorithms, such as sequential search), our goal is to find a position where either x appears or, if it does not, we can be sure that it appears nowhere. The invariant says that we have an interval with the same properties (either x appears at a position belonging to that interval or, if it does not, it appears nowhere). It obviously includes the goal as a special case: if the interval has length 1, it defines a single position.

An invariant should be:

  1. Strong enough that we can devise an exit condition which in the end, combined with the invariant, gives us the goal we seek (a solution).
  2. Weak enough that we can devise an initialization that ensures it (by shooting into the yellow area) easily.
  3. Tuned so that we can devise a loop body that, from a state satifying the invariant, gets us to a new one that is closer to the goal.

In the example:

  1. The exit condition is simply that the interval’s length is 1. (Technically, that we have computed Result as the single interval element.) Then from the invariant and the exit condition, we get the goal we want.
  2. Initialization is easy, since we can just take the initial interval to be the whole index range of the array, which trivially satisfies the invariant.
  3. The loop body simply decreases the length of the interval (which can serve as loop variant to ensure termination). How we decrease the length depends on the search strategy; in sequential search, each iteration decreases the length by 1, correct although not fast, and binary search decreases it by about half.

The general scheme always applies. Every loop algorithm is characterized by an invariant. The invariant may be called the DNA of the algorithm.

To demonstrate the relevance of this principle, my colleagues Furia, Velder, and I published a survey paper [6] in ACM Computing Surveys describing the invariants of important algorithms in many areas of computer science, from search algorithms to sorting (all major algorithms), arithmetic (long integer addition, squaring), optimization and dynamic programming  (Knapsack, Levenshtein/Edit distance), computational geometry (rotating calipers), Web (Page Rank)… I find it pleasurable and rewarding to go deeper into the basis of loop algorithms and understand their invariants; like a geologist who does not stop at admiring the mountain, but gets to understand how it came to be.

Such techniques are inevitable if we want to get our programs right, the topic of this article. Even putting aside the Bloch average-computation overflow issue, I started with 5 program attempts, all kind of friendly-looking but wrong in different ways. I could have continued fiddling with the details, following my gut feeling to fix the flaws and running more and more tests. Such an approach can be reasonable in some cases (if you have an algorithm covering a well-known and small set of cases), but will not work for non-trivial algorithms.

Newcomers to the concept of loop invariant sometimes panic: “this is all fine, you gave me the invariants in your examples, how do I find my own invariants for my own loops?” I do not have a magic  recipe (nor does anyone else), but there is no reason to be scared. Once you have understood the concept and examined enough examples (just a few of those in [6] should be enough), writing the invariant at the same time as you are devising a loop will come as a second nature to you.

As the fumbling attempts in the first few sections should show, there is not much of an alternative. Try this approach. If you are reaching these final lines after reading what preceded them, allow me to thank you for your patience, and to hope that this rather long chain of reflections on verification will have brought you some new insights into the fascinating challenge of writing correct programs.

References

[1] Nadia Polikarpova, Julian Tschannen, and Carlo A. Furia: A Fully Verified Container Library, in Proceedings of 20th International Symposium on Formal Methods (FM 15), 2015. (Best paper award.)

[2] Martin Nordio, Cristiano Calcagno, Peter Müller and Bertrand Meyer: A Sound and Complete Program Logic for Eiffel, in Proceedings of TOOLS 2009 (Technology of Object-Oriented Languages and Systems), Zurich, June-July 2009, eds. M. Oriol and B. Meyer, Springer LNBIP 33, June 2009.

[3] Boogie page at MSR, see here for publications and other information.

[4] Z3 was also originally from MSR and has been open-sourced, one can get access to publications and other information from  its Wikipedia page.

[5] Carlo Furia, Bertrand Meyer and Sergey Velder: Loop invariants: Analysis, Classification and Examples, in ACM Computing Surveys, vol. 46, no. 3, February 2014. Available here.

[6] Dynamic programming is a form of recursion removal, turning a recursive algorithm into an iterative one by using techniques known as “memoization” and  “bottom-up computation” (Berry). In this transformation, the invariant plays a key role. I will try to write this up some day as it is a truly elegant and illuminating explanation.

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Notations you didn’t even know you could use

Consider the following expression:

∃ c: s   ¦   moisture (c) = soft

This is obviously mathematics. To express such a property in a programming language, you have to write a function containing a loop that iterates through the elements of s. Right?

Wrong. The above construct is valid Eiffel. It’s a consequence of recent syntax extensions that retain all the simplicity and consistency of the language but take full advantage of Unicode. Of course you do not have Unicode characters such as on you keyboard, but EiffelStudio’s completion mechanism inserts them for you.

To see how this works, just read Alexander Kogtenkov’s recent blog post on the topic.

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Getting your priorities right

In the restrooms of French freeway service stations managed by Total, the soap dispensers partake of pressing advice:

Not too much soap please

The message reads:

ONLY ONCE
Press for
clean hands
1X

Total wants to save on costs. Soap is money.

Fine. But on the matter of hand-washing one might (perhaps) think, in the current circumstances, of more urgent advice?

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Call for suggestions: beauty

On April 29 in the early evening at the Schaffhausen Institute of Technology I will give a talk on “The Beauty of Software”, exploring examples of what makes some concepts, algorithms, data structures etc. produce a sense of esthetics. (Full abstract below.) I gave a first version at TOOLS last year but am revising and expanding the talk extensively.

I obviously have my own examples but am interested in more. If you have some that you feel should be considered for inclusion, perhaps because you experienced a “Wow!” effect when you encountered them, please tell me. I am only asking for names or general pointers, not an in-depth analysis (that’s my job). To avoid having my thunder stolen I would prefer that you alert me by email. I will give credit for examples not previously considered.

Thanks!

Abstract of the talk as published:

Scientists often cite the search for beauty as one of their primary guiding forces. Programming and software engineering offer an inexhaustible source of astoundingly beautiful ideas, from strikingly elegant algorithms and data structures to powerful principles of methodology and language design.

Defining beauty is elusive, but true beauty imposes itself in such a way as to remove any doubt. Drawing comparisons from art, literature and other endeavours. He will show a sample of ideas from all walks of software, directly understandable to a wide audience of non-software-experts, offering practical applications in technology that we use daily, and awe-inspiring in their simplicity and elegance.

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LASER 2020 in Elba Island: DevOps, Microservices and more, first week of June

The page for the 2020 LASER summer school (31 May to 7 June) now has the basic elements (some additions still forthcoming) and registration at the early price is open. The topic is DevOps, Microservices and Software Development for the Age of the Web with both conceptual lectures and contributions from industry, by technology leaders from Amazon, Facebook and ServiceNow. The confirmed speakers are:

  • Fabio Casati, ServiceNow and University of Trento, and Kannan Govindarajan from ServiceNow on Taking AI from research to production – at scale.
  • Adrian Cockcroft, Amazon Web Services, on Building and Operating Modern Applications.
  • Elisabetta Di Nitto, Politecnico di Milano.
  • Valérie Issarny, INRIA, on The Web for the age of the IoT.
  • Erik Meijer, Facebook, on Software Development At Scale.
  • Me, on Software from beginning to end: a comprehensive method.

As always, the setup is the incomparable environment of the Hotel del Golfo in Procchio, Elba Island off the coast of Tuscany, ideal at that time of year (normally good weather, warm but not hot, few tourists). The school is intensive but there is time to enjoy the beach, the hotel’s amenities and the wonderful of environment of Elba (wake up your inner Napoleon). The school has a fairly small size and everyone lives under the same (beautiful) roof, so there is plenty of time for interaction with the speakers and other participants.

About these participants: the school is intended for engineers and managers in industry as well as researchers and PhD student. In fact it’s a mix that one doesn’t find that often, allowing for much cross-learning.

Another way to put it is that this is now the 16th edition of the school (it started in 2004 but we skipped one year), so it cannot be doing everything wrong.

 

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Talk on requirements at UC Santa Barbara tomorrow

I am giving a “distinguished lecture” at the University of California, Santa Barbara, January 10 (Friday, tomorrow) at 14. The title is A Comprehensive Approach to Requirements Engineering.

The abstract and rest of the information are here.

I will spend the last few minutes of the talk discussing other current developments (verification, concurrency).

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This Wednesday in Nice: survey talk on the Eiffel method

The “Morgenstern Colloquium” at the University of Nice / INRIA Sophia Antipolis invited me to give a talk, next Wednesday (18 December) at 11 in Sophia Antipolis, in the aptly named* “Kahn Building”. The announcement appears here. I proposed various topics but (pleasant surprise) the organizers explicitly asked me to lecture about what I really want to talk about: the Eiffel approach. I will give a general presentation describing not specifically the language but the unified view of software construction embodied in Eiffel, from modeling to requirements to design, implementation and verification. Here is the abstract:

With society’s growing reliance on IT systems, the ability to write high-quality software is ever more critical. While a posteriori verification techniques have their role, there is no substitute for methods and tools that provide built-in quality (“correctness by construction”) and scale up to very large systems. For several decades my colleagues and I have been building such a method, based in particular on the concept of Design by Contract, the associated tools and the supporting language, Eiffel. The scope is wide, encompassing all aspects of the software development process, from requirements and design to implementation and verification. I will present an overview of the approach, show what it can yield, and discuss remaining open issues.

This talk is meant for everyone, whether from industry or academia, with an interest in practical techniques for engineering high-quality software.

No registration is required. The presentation will be in English.

Note

*Gilles Kahn, a brilliant computer scientist who died too young, was for a while director of INRIA.

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Defining and classifying requirements (new publication)

Software engineering has improved a lot in the past couple of decades, but there remains an area where the old doomsday style of starting a software engineering paper (software crisis, everything is rotten…) still fits: requirements engineering. Just see the chasm between textbook advice and the practice of most projects.

I have written on requirements in this blog, including very recently, and will continue in forthcoming installments. For today I  want to point to a recent article [1],  presented at the newly revived TOOLS conference in October. It attempts to bring some order and rigor to the basic definitions in the field.

From the abstract:

Requirements engineering is crucial to software development but lacks a precise definition of its fundamental concepts. Even the basic definitions in the literature and in industry standards are often vague and verbose.

To remedy this situation and provide a solid basis for discussions of requirements, this work provides precise definitions of the fundamental requirements concepts and two systematic classifications: a taxonomy of requirement elements (such as components, goals, constraints…) ; and a taxonomy of possible relations between these elements (such as “extends”, “excepts”, “belongs”…).

The discussion evaluates the taxonomies on published requirements documents; readers can test the concepts in two online quizzes.

The intended result of this work is to spur new advances in the study and practice of software requirements by clarifying the fundamental concepts.

This version is a first step; we are aware of its limitations and are already revising the definitions and taxonomy. The project is aimed at providing a solid foundation for a delicate area of software engineering and it will take some time to get it completely right. Still, I think the paper as it is already introduces important concepts. I will within the next two weeks write a more detailed blog article summarizing some of them.

References

[1] Bertrand Meyer, Jean-Michel Bruel, Sophie Ebersold, Florian Galinier, Alexandr Naumchev, The Anatomy of Requirements, in TOOLS 51, Software Technology: Methods and Tools
Innopolis, Russia, October 15–17, 2019, pages 10-40, available here (Springer site, paywall) and here (arXiv draft).

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What happened to the kilogram? Schaffhausen, 16 December

December 16 (next Monday), the newly created Schaffhausen Institute of Technology organizes an entire day of events around three (no less) talks by the physics Nobel prize winner and MIT professor Wolfgang Ketterle.

The culmination of the day is a talk by Prof. Ketterle in the evening on “What happened to the kilogram?”. From the abstract:

For 130 years, a cylinder made of a platinum-iridium alloy stored in Saint-Cloud near Paris was the official definition of a kilogram, the basic unit of mass. This all changed on May 20 of this year: a kilo is now be defined by a fundamental constant of nature known, the Planck constant, which relates the energy of a photon to its frequency: 6.62607015 times 10-34 kilograms times square meters per second. Try that the next time you buy a kilo of asparagus.

Sounds complicated? For MIT’s Wolfgang Ketterle, a Nobel Prize winner, “Conceptually, the definition is very simple”.

Simple? Really? Come to Schaffhausen and hear for yourself whether Prof. Ketterle can make the new kilogram crystal-clear to common mortals.

Earlier in the day, he will give a talk in German on new forms of materials that appear at temperatures near the absolute zero, complete with demonstrations.

More generally, there is a full set of talks throughout the day about various aspects of advanced physics and computer science, and even a “quantum magician”, plus music and food.

Schaffhausen is about 40 minutes from Zurich (or Zurich airport) by train or car.

Attendance is free but registration is recommended. One can register for the full day or for some events only. See further information and registration form here.

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Are my requirements complete?

Some important concepts of software engineering, established over the years, are not widely known in the community. One use of this blog is to provide tutorials on such overlooked ideas. An earlier article covered one pertaining to project management: the Shortest Possible Schedule property . Here is another, this time in the area of requirements engineering, also based on a publication that I consider to be a classic (it is over 40 years old) but almost unknown to practitioners.

Practitioners are indeed, as in most of my articles, the intended audience. I emphasize this point right at the start because if you glance at the rest of the text you will see that it contains (horror of horrors) some mathematical formulae, and might think “this is not for me”. It is! The mathematics is very simple and my aim is practical: to shed light on an eternal question that faces anyone writing requirements (whatever the style, traditional or agile): how can I be sure that a requirements specification is complete?

To a certain extent you cannot. But there is better answer, a remarkably simple one which, while partial, helps.

Defining completeness

The better answer is called “sufficient completeness” and comes from the theory of abstract data types. It was introduced in a 1978 article by Guttag and Horning [1]. It is also implicit in a more down-to-earth document, the 1998 IEEE standard on how to write requirements [2].

There is nothing really new in the present article; in fact my book Object-Oriented Software Construction [3] contains an extensive discussion of sufficient completeness (meant to be more broadly accessible than Guttag and Horning’s scholarly article). But few people know the concepts; in particular very few practitioners have heard of sufficient completeness (if they have heard at all of abstract data types). So I hope the present introduction will be useful.

The reason the question of determining completeness of requirements seems hopeless at first is the natural reaction: complete with respect to what? To know that the specification is complete we would need a more general description of all that our stakeholders want and all the environment constraints, but this would only push the problem further: how do we know that such description itself is complete?

That objection is correct in principle: we can never be sure that we did not forget something someone wanted, or some property that the environment imposes. But there also exist more concrete and assessable notions of completeness.

The IEEE standard gives three criteria of completeness. The first states that “all requirements” have been included, and is useless, since it  runs into the logical paradox mentioned above, and is tautological anyway (the requirements are complete if they include all requirements, thank you for the information!). The second is meaningful but of limited interest (a “bureaucratic” notion of completeness): every element in the requirements document is numbered, every cross-reference is defined and so on. The last criterion is the interesting one: “Definition of the responses of the software to all realizable classes of input data in all realizable classes of situations”. Now this is meaningful. To understand this clause we need to step back to sufficient completeness and, even before that, to abstract data types.

Abstract data types will provide our little mathematical excursion (our formal picnic in the words of an earlier article) in our study of requirements and completeness. If you are not familiar with this simple mathematical theory, which every software practitioner should know, I hope you will benefit from the introduction and example. They will enable us to introduce the notion of sufficient completeness formally before we come back to its application to requirements engineering.

Specifying an abstract data type

 Abstract data types are the mathematical basis for object-oriented programming. In fact, OO programming but also OO analysis and OO design are just a realization of this mathematical concept at various levels of abstraction, even if few OO practitioners are aware of it. (Renewed reference to [3] here if you want to know more.)

An ADT (abstract data type) is a set of objects characterized not by their internal properties (what they are) but by the operations applicable to them (what they have), and the properties of these operations. If you are familiar with OO programming you will recognize that this is exactly, at the implementation level, what a class is. But here we are talking about mathematical objects and we do not need to consider implementation.

An example  of a type defined in this way, as an ADT, is a notion of POINT on a line. We do not say how this object is represented (a concept that is irrelevant at the specification level) but how it appears to the rest of the world: we can create a new point at the origin, ask for the coordinate of a point, or move the point by a certain displacement. The example is the simplest meaningful one possible, but it gives the ideas.

adt

An ADT specification has three part: Functions, Preconditions and Axioms. Let us see them (skipping Preconditions for the moment) for the definition of the POINT abstract data type.

The functions are the operations that characterize the type. There are three kinds of function, defined by where the ADT under definition, here POINT, appears:

  • Creators, where the type appears only among the results.
  • Queries, where it appears only among the arguments.
  • Commands, where it appears on both sides.

There is only one creator here:

new: → POINT

new is a function that takes no argument, and yields a point (the origin). We will write the result as just new (rather than using empty parentheses as in new ()).

Creators correspond in OO programming to constructors of a class (creation procedures in Eiffel). Like constructors, creators may have arguments: for example instead of always creating a point at the origin we could decide that new creates a point with a given coordinate, specifying it as INTEGER → POINT and using it as new (i) for some integer i (our points will have integer coordinates). Here for simplicity we choose a creator without arguments. In any case the new type, here POINT, appears only on the side of the results.

Every useful ADT specification needs at least one creator, without which we would never obtain any objects of the type (here any points) to work with.

There is also only one query:

x: POINT → INTEGER

 which gives us the position of a point, written x (p) for a point p. More generally, a query enables us to obtain properties of objects of the new type. These properties must be expressed in terms of types that we have already defined, like INTEGER here. Again there has to be at least one query, otherwise we could never obtain usable information (information expressed in terms of what we already know) about objects of the new type. In OO programming, queries correspond to fields (attributes) of a class and functions without side effects.

And we also have just one command:

move: POINT × INTEGER → POINT

a function that for any point p and integer i and yields a new point, move (p, i).  Again an ADT specification is not interesting unless it has at least one command, representing ways to modify objects. (In mathematics we do not actually modify objects, we get new objects. In imperative programming we will actually update existing objects.) In the classes of object-oriented programming, commands correspond to procedures (methods which may change objects).

You see the idea: define the notion of POINT through the applicable operations.

Listing their names and the types of their arguments types results (as in POINT × INTEGER → POINT) is not quite enough to specify these operations: we must specify their fundamental properties, without of course resorting to a programming implementation. That is the role of the second component of an ADT specification, the axioms.

For example I wrote above that new yields the origin, the point for which x = 0,  but you only had my word for it. My word is good but not good enough. An axiom will give you this property unambiguously:

x (new) = 0                                    — A0

The second axiom, which is also the last, tells us what move actually does. It applies to any point p and any integer m:

x (move (p, m)) = x (p) + m       — A1

In words: the coordinate of the point resulting from moving p by m is the coordinate of p plus m.

That’s it! (Except for the notion of precondition, which will wait a bit.) The example is trivial but this approach can be applied to any number of  data types, with any number of applicable operations and any level of complexity. That is what we do, at the design and implementation level, when writing classes in OO programming.

Is my ADT sufficiently complete?

Sufficient completeness is a property that we can assess on such specifications. An ADT specification for a type T (here POINT) is sufficiently complete if the axioms are powerful enough to yield the value of any well-formed query expression in a form not involving T. This definition contains a few new terms but the concepts are very simple; I will explain what it means through an example.

With an ADT specification we can form all kinds of expressions, representing arbitrarily complex specifications. For example:

x (move (move (move (new, 3), x (move (move (new, -2), 4))), -6))

This expression will yield an integer (since function x has INTEGER as its result type) describing the result of a computation with points. We can visualize this computation graphically; note that it involves creating two points (since there are two occurrences of new) and moving them, using in one case the current coordinate of one of them as displacement for the other. The following figure illustrates the process.

computation

The result, obtained informally by drawing this picture, is the x of P5, that is to say -1. We will derive it mathematically below.

Alternatively, if like most programmers (and many other people) you find it more intuitive to reason operationally than mathematically, you may think of the previous expression as describing the result of the following OO program (with variables of type POINT):

create p                                — In C++/Java syntax: p = new POINT();
create q
p.move (3)
q.move (-2)
q.move (4)
p.move (q.x)
p.move (-6)

Result := p.x

You can run this program in your favorite OO programming language, using a class POINT with new, x and move, and print the value of Result, which will be -1.

Here, however, we will stay at the mathematical level and simplify the expression using the axioms of the ADT, the same way we would compute any other mathematical formula, applying the rules without needing to rely on intuition or operational reasoning. Here is the expression again (let’s call it i, of type INTEGER):

ix (move (move (move (new, 3), x (move (move (new, -2), 4))), -6))

A query expression is one in which the outermost function being applied, here x, is a query function. Remember that a query function is one which the new type, here POINT, appears only on the left. This is the case with x, so the above expression i is indeed a query expression.

For sufficient completeness, query expressions are the ones of interest because their value is expressed in terms of things we already know, like INTEGERs, so they are the only way we can concretely obtain directly usable information the ADT (to de-abstract it, so to speak).

But we can only get such a value by applying the axioms. So the axioms are “sufficiently complete” if they always give us the answer: the value of any such query expression.

 Let us see if the above expression i satisfies this condition of sufficient completeness. To make it more tractable let us write  it in terms of simpler expressions (all of type POINT), as illustrated by the figure below:

p1 = move (new, 3)
p2= move (new, -2)
p3= move (p2, 4)
p4= move (p1, x (p3))
p5= move (p4, -6)
i = x (p5)

expression

(You may note that the intermediate expressions roughly correspond to the steps in the above interpretation of the computation as a program. They also appear in the illustrative figure repeated below.)

computation

Now we start applying the axioms to evaluating the expressions. Remember that we have two axioms: A0 tells us that x (new) = 0 and A1 that x (move (p, m)) = x (p) + m. Applying A1 to the definition the expression i yields

i = x (p4) – 6
= i4 – 6

if we define

i4 = x (p4)      — Of type INTEGER

We just have to compute i4. Applying A1 to the definion of p4 tells us that

i4 = x (p1) + x (p3)

To compute the two terms:

  • Applying A1 again, we see that the first term x (p1) is x (new) + 3, but then A0 tells us that x (new) is zero, so x (p1) is 3.
  • As to x (p3), it is, once more from A1, x (p2) + 4, and x (p2) is (from A1 then A0), just -2, so x (p3) is 2.

In the end, then, i4 is 5, and the value of the entire expression i = i4 – 6 is -1. Good job!

Proving sufficient completeness

The successful computation of i was just a derivation for one example, showing that in that particular case the axioms yield the answer in terms of an INTEGER. How do we go from one example to an entire specification?

The bad news first: like all interesting problems in programming, sufficient completeness of an ADT specification is theoretically undecidable. There is no general automatic procedure that will process an ADT specification and print out ““sufficiently complete” or “not sufficiently complete”.

Now that you have recovered from the shock, you can share the computer scientist’s natural reaction to such an announcement: so what. (In fact we might define the very notion of computer scientist as someone who, even before he brushes his teeth in the morning — if he brushes them at all — has already built the outline of a practical solution to an undecidable problem.) It is enough that we can find a way to determine if a given specification is sufficiently complete. Such a proof is, in fact, the computer scientist’s version of dental hygiene: no ADT is ready for prime time unless it is sufficiently complete.

The proof is usually not too hard and will follow the general style illustrated for our simple example.

We note that the definition of sufficient completeness said: “the axioms are powerful enough to yield the value of any well-formed query expression in a form not involving the type”. I have not defined “well-formed” yet. It simply means that the expressions are properly structured, with the proper syntax (basically the correct matching of parentheses) and proper number and types of arguments. For example the following are not well-formed (if p is an expression of type POINT):

move (p, 55(     — Bad use of parentheses.
move (p)            — Wrong number of arguments.
move (p, p)       — Wrong type: second argument should be an integer.

Such expressions are nonsense, so we only care about well-formed expressions. Note that in addition to new, x and move , an expression can use integer constants as in the example (although we could generalize to arbitrary integer expressions). We consider an integer constant as a query expression.

We have to prove that with the two axioms A0 and A1 we can determine the value of any query expression i. Note that since the only query functions is x, the only possible form for i, other than an integer constant, is x (p) for some expression p of type POINT.

The proof proceeds by induction on the number n of parenthesis pairs in a query expression i.

There are two base steps:

  • n = 0: in that case i can only be an integer constant. (The only expression with no parentheses built out of the ADT’s functions is new, and it is not a query expression.) So the value is known. In all other cases i will be of the form x (p) as noted.
  • n = 1: in that case p  can only be new, in other words i = x (new), since the only function that yields points, other than new, is move, and any use of it would add parentheses. In this case axiom A0 gives us the value of i: zero.

For the induction step, we consider i with n + 1 parenthesis pairs for n > 1. As noted, i is of the form x (p), so p has exactly n parenthesis pairs. p cannot be new (which would give 0 parenthesis pairs and was taken care of in the second base step), so p has to be of the form

p =  move (p’, i’)    — For expressions p’ of type POINT and i’ of type INTEGER.

implying (since i = x (p)) that by axiom A1, the value of i is

x (p’) + i’

So we will be able to determine the value of i if we can determine the value of both x (p’) and i’. Since p has n parenthesis pairs and p =  move (p’, i’), both p’ and i’ have at most n – 1 parenthesis pairs. (This use of n – 1 is legitimate because we have two base steps, enabling us to assume n > 1.) As a consequence, both x (p’) and i’ have at most n parenthesis pairs, enabling us to deduce their values, and hence the value of i, by the induction hypothesis.

Most proofs of sufficient completeness in my experience follow this style: induction on the number of parenthesis pairs (or the maximum nesting level).

Preconditions

I left until now the third component of a general ADT specification: preconditions. The need for preconditions arises because most practical specifications need some of their functions to be partial. A partial function from X to Y is a function that may not yield a value for some elements of X. For example, the inverse function on real numbers, which yields 1 / a for x, is partial  since it is not defined for a = 0 (or, on a computer, for non-zero but very small a).

Assume that in our examples we only want to accept points that lie in the interval [-4, +4]:

limited

 We can simply model this property by turning move into a partial function. It was specified above as

move: POINT × INTEGER → POINT

The ordinary arrow → introduces a total (always defined) function. For a partial function we will use a crossed arrow ⇸, specifying the function as

move: POINT × INTEGER ⇸ POINT

Other functions remain unchanged. Partial functions cause trouble: for f in X ⇸ Y we can no longer cheerfully use f (x) if f is a partial function, even for x of the appropriate type X. We have to make sure that x belongs to the domain of f, meaning the set of values for which f is defined. There is no way around it: if you want your specification to be meaningful and it uses partial functions, you must specify explicitly the domain of each of them. Here is how to do it, in the case of move:

move (p: POINT; d: INTEGER) require |x (p) + d | < 5    — where |…| is absolute value

To adapt the definition (and proofs) of sufficient completeness to the possible presence of partial functions:

  • We only need to consider (for the rule that axioms must yield the value of query expressions) well-formed expressions that satisfy the associated preconditions.
  • The definition must, however, include the property that axioms always enable us to determine whether an expression satisfies the associated preconditions (normally a straightforward part of the proof since preconditions are themselves query expressions).

Updating the preceding proof accordingly is not hard.

Back to requirements

The definition of sufficient completeness is of great help to assess the completeness of a requirements document. We must first regretfully note that for many teams today requirements stop at  “use cases” (scenarios) or  “user stories”. Of course these are not requirements; they only describe individual cases and are to requirements what tests are to programs. They can serve to check requirements, but do not suffice as requirements. I am assuming real requirements, which include descriptions of behavior (along with other elements such as environment properties and project properties). To describe behaviors, you will define operations and their effects. Now we know what the old IEEE standard is telling us by stating that complete requirements should include

definition of the responses of the software to all realizable classes of input data in all realizable classes of situations

Whether or not we have taken the trouble to specify the ADTs, they are there in the background; our system’s operations reflect the commands, and the effects we can observe reflect the queries. To make our specification complete, we should draw as much as possible of the (mental or explicit) matrix of possible effects of all commands on all queries. “As much as possible” because software engineering is engineering and we will seldom be able to reach perfection. But the degree of fullness of the matrix tells us a lot (possible software metric here?) about how close our requirements are to completeness.

I should note that there are other aspects to completeness of requirements. For example the work of Michael Jackson, Pamela Zave and Axel van Lamsweerde (more in some later article, with full references) distinguishes between business goals, environment constraints and system properties, leading to a notion of completeness as how much the system properties meet the goals and obey the constraints [4]. Sufficient completeness operates at the system level and, together with its theoretical basis, is one of those seminal concepts that every practicing software engineer or project manager should master.

References and notes

[1] John V. Guttag, Jim J. Horning: The Algebraic Specification of Abstract Data Types, in Acta Informatica, vol. 10, no. 1, pages 27-52, 1978, available here from the Springer site. This is a classic paper but I note that few people know it today; in Google Scholar I see over 700 citations but less than 100 of them in the past 8 years.

[2]  IEEE: Recommended Practice for Software Requirements Specifications, IEEE Standard 830-1998, 1998. This standard is supposed to be obsolete and replaced by newer ones, more detailed and verbose, but it remains the better reference: plain, modest and widely applied by the industry. It does need an update, but a good one.

[3] Bertrand Meyer, Object-Oriented Software Construction, 2nd edition, Prentice Hall, 1997. The discussion of sufficient completeness was in fact already there in the first edition from 1988.

[4] With thanks to Elisabetta Di Nitto from Politecnico di Milano for bringing up this notion of requirements completeness.

Recycled A version of this article was first published on the Communications of the ACM blog.

 

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June LASER school, Elba, on Devops, Microservices…

The 2020 LASER summer school has been announced. It will take place June 1 to 6* , as always in Elba Island, this year with the theme DevOps, Microservices and Software Development for the Age of the Web. The first five speakers are listed on the conference page, with more to come, from both academia and industry.

This is the 16th edition of the school (already) and, as always, rests on the LASER recipe of “Sea, Sun and Software”: densely packed lectures by top experts with the opportunity to enjoy the extraordinary surroundings of the Island of Elba and the Hotel del Golfo’s unique food, beach and facilities, with lots of time devoted to interactions between speakers and attendees.

This year’s theme is devoted to advances in the newest Web technologies and the corresponding software engineering issues and development models.

*Arrival on May 31st, departure on June 7th.

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Formality in requirements: new publication

The best way to make software requirements precise is to use one of the available “formal” approaches. Many have been proposed; I am not aware of a general survey published so far. Over the past two years, we have been working on a comprehensive survey of the use of formality in requirements, of which we are now releasing a draft. “We” is a joint informal research group from Innopolis University and the University of Toulouse, whose members have been cooperating on requirements issues, resulting in publications listed  under “References” below and in several scientific events.

The survey is still being revised, in particular because it is longer than the page limit of its intended venue (ACM Computing Surveys), and some sections are in need of improvement. We think, however, that the current draft can already provide a solid reference in this fundamental area of software engineering.

The paper covers a broad selection of methods, altogether 22 of them, all the way from completely informal to strictly formal. They are grouped into five categories: natural language, semi-formal, automata- or graph-based, other mathematical frameworks, programming-language based. Examples include SysML, Relax, Statecharts, VDM, Eiffel (as a requirements notation), Event-B, Alloy. For every method, the text proposes a version of a running example (the Landing Gear System, already used in some of our previous publications) expressed in the corresponding notation. It evaluates the methods using a set of carefully defined criteria.

The paper is: Jean-Michel Bruel, Sophie Ébersold, Florian Galinier, Alexandr Naumchev, Manuel Mazzara and Bertrand Meyer: Formality in Software Requirements, draft, November 2019.

The text is available here. Comments on the draft are welcome.

References

Bertrand Meyer, Jean-Michel Bruel, Sophie Ebersold, Florian Galinier and Alexandr Naumchev: Towards an Anatomy of Software Requirements, in TOOLS 2019, pages 10-40, see here (or arXiv version here). I will write a separate blog article about this publication.

Alexandr Naumchev and Bertrand Meyer: Seamless requirements. Computer Languages, Systems & Structures 49, 2017, pages 119-132, available here.

Florian Galinier, Jean-Michel Bruel, Sophie Ebersold and Bertrand Meyer: Seamless Integration of Multirequirements, in Complex Systems, 25th International Requirements Engineering Conference Workshop, IEEE, pages 21-25, 2017, available here.

Alexandr Naumchev, Manuel Mazzara, Bertrand Meyer, Jean-Michel Bruel, Florian Galinier and Sophie Ebersold: A contract-based method to specify stimulus-response requirements, Proceedings of the Institute for System Programming, vol. 29, issue 4, 2017, pp. 39-54. DOI: 10.15514, available here.

Alexandr Naumchev and Bertrand Meyer: Complete Contracts through Specification Drivers., in 10th International Symposium on Theoretical Aspects of Software Engineering (TASE), pages 160-167, 2016, available here.

Alexandr Naumchev, Bertrand Meyer and Víctor Rivera: Unifying Requirements and Code: An Example, in PSI 2015 (Ershov conference, Perspective of System Informatics), pages 233-244, available here.

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Publications on CS/SE/informatics education

Recently I had a need to collect my education-related publications, so I went through my publication list and extracted items devoted to issues of learning computer science (informatics) and software engineering. There turned out to be far more than I expected; I did not think of myself as primarily an education researcher but it seems I am that too. (Looking around in my fields and institutions I don’t immediately see other research computer scientists with a comparable record at SIGCSE, ITiCSE and other top CS education publications.)

Without presuming that the list will be of interest I am reproducing it below for the record. All comes from my publication list here, which contains more information, in particular a descriptive paragraph or two for every single publication. (Note: that consolidate list is up to date until 2017 only, because it is produced by a script and one of the supporting system tools — not an Eiffel program, thanks for asking — has stopped functioning on the latest OS version on the server. I have not found the time to fix it yet. There are a good dozen publications missing for 2018 and 2019.)

I have also included PhD theses in education. (Whole list of PhD theses supervised here.)

The topics include among others, in approximate chronological order (although the list below is in the reverse order):

    • Early experience teaching modern programming concepts in both industry and universities.
    • In the nineties, I was full time at Eiffel Software, the development of a general framework for teaching programming. This was written from the safe position of someone in industry advising academic colleagues on what to do (usually the advice goes the other way). I did have, however, the opportunity to practice my preaching in short stints at University of Technology, Sydney and  particularly Monash University. The concept of the Inverted Curriculum (also known as “ Outside-In”) date back to that period, with objects first (actually classes) and contracts first too.
    • When I joined ETH, a general paper on the fundamental goals and concepts of software engineering education, “Software Engineering in the Academy”, published in IEEE Computer.
    • At ETH, putting the Inverted Curriculum in practice, with 14 consecutive sessions of the introductory programming courses for all computer science students, resulting in the Touch of Class textbook and a number of papers coming out of our observations. An estimated 6000 students took the course. A variant of it has also been given several times at Innopolis University.
    • A theory of how to structure knowledge for educational purposes, leading to the notion of “Truc” (Teachable, Reusable Unit of Cognition).
    • The development by Michela Pedroni of the Trucstudio environment, similar in its form to an IDE but devoted, instead of the development of programs, to the visual development of courses, textbooks, curricula etc.
    • Empirical work by Marie-Hélène Ng Cheong Vee (Nienaltowski) and Michela Pedroni on what beginners understand easily, and not, for example according to the phrasing of compiler error messages.
    • Other empirical work, by Michela Pedroni and Manuel Oriol, on the prior knowledge of entering computer science students.
    • The DOSE course (Distributed and Outsourced Software Engineering) ran for several years a student project done by joint student teams from several cooperating universities, including Politecnico di Milano which played a key role along with us. It enabled many empirical studies on the effect on software development of having geographically distributed teams. People who played a major role in this effort are, at ETH, Martin Nordio, Julian Tschannen and Christian Estler and, at Politecnico, Elisabetta di Nitto, Giordano Tamburrelli and Carlo Ghezzi.
    • Several MOOCs, among the first at ETH, on introductory computing and agile methods. They do not appear below because they are not available at the moment on the EdX site (I do not know why and will try to get them reinstated). The key force there was Marco Piccioni. MOOCs are interesting for many reasons; they are a substitute neither for face-to-face teaching nor for textbooks, but an interesting complement offering novel educational possibilities. Our programming MOOCs are particularly innovative since (thanks to codeboard, see below) they provide the opportunity to compile and run program directly from the course exercise pages, compare the run’s result to correct answers for prepared tests, and get immediate feedback .
    • A comparative study of teaching effectiveness of two concurrency models, Eiffel SCOOP and JavaThreads (Sebastian Nanz, Michela Pedroni).
    • The development (Christian Estler with Martin Nordio) of the Codeboard system and site, still as far as I know the most advanced system for cloud support to teach programming, enabling students to compile, correct and run programs on the web, with support for various languages. Codeboard is used in the programming MOOCs.
    • A hint system (Paolo Antonucci, Michela Pedroni) to help students get progressive help, as in video games, when they stumble trying to write a program, e.g. with Codeboard.

Supervised PhD theses on education

The following three theses are devoted to educational topics (although many of the  other theses have educational aspects too):

Christian Estler, 2014, Understanding and Improving Collaboration in Distributed Software Development, available here.

Michela Pedroni, 2009, Concepts and Tools for Teaching Programming, available here.

Markus Brändle, 2006: GraphBench: Exploring the Limits of Complexity with Educational Software: Exploring the limits of complexity with educational software, available here. (The main supervisor in this case was Jürg Nievergelt.)

MOOCs (Massive Online Open Courses)

Internal MOOCs, and three courses on EdX (links will be added when available):

  • Computing: Art, Magic, Science? Part 1 (CAMS 1), 2013.
  • Computing: Art, Magic, Science? Part 1 (CAMS 2), 2014.
  • Agile Software Development, 2015.

Publications about education

1. Paolo Antonucci, Christian Estler, Durica Nikolic, Marco Piccioni and Bertrand Meyer: An Incremental Hint System For Automated Programming Assignments, in ITiCSE ’15, Proceedings of 2015 ACM Conference on Innovation and Technology in Computer Science Education, 6-8 July 2015, Vilnius, ACM Press, pages 320-325. (The result of a master’s thesis, a system for helping students solve online exercises, through successive hints.) Available here.

2. Jiwon Shin, Andrey Rusakov and Bertrand Meyer: Concurrent Software Engineering and Robotics Education, in 37th International Conference on Software Engineering (ICSE 2015), Florence, May 2015, IEEE Press, pages 370-379. (Describes our innovative Robotics Programming Laboratory course, where students from 3 departments, CS, Mechanical Engineering and Electrical Engineering learned how to program robots.) Available here.

3. Cristina Pereira, Hannes Werthner, Enrico Nardelli and Bertrand Meyer: Informatics Education in Europe: Institutions, Degrees, Students, Positions, Salaries — Key Data 2008-2013, Informatics Europe report, October 2014. (Not a scientific publication but a report. I also collaborated in several other editions of this yearly report series, which I started, from 2011 on. A unique source of information about the state of CS education in Europe.) Available here.

4. (One of the authors of) Informatics education: Europe cannot afford to miss the boat, edited by Walter Gander, joint Informatics Europe and ACM Europe report, April 2013. An influential report which was instrumental in the introduction of computer science in high schools and primary schools in Europe, particularly Switzerland. Emphasized the distinction between “digital literacy” and computer science. Available here.

5. Sebastian Nanz, Faraz Torshizi, Michela Pedroni and Bertrand Meyer: Design of an Empirical Study for Comparing the Usability of Concurrent Programming Languages, in Information and Software Technology Journal Elsevier, volume 55, 2013. (Journal version of conference paper listed next.) Available here.

6. Bertrand Meyer: Knowledgeable beginners, in Communications of the ACM, vol. 55, no. 3, March 2012, pages 10-11. (About a survey of prior knowledge of entering ETH CS students, over many years. Material from tech report below.) Available here.

7. Sebastian Nanz, Faraz Torshizi, Michela Pedroni and Bertrand Meyer: Design of an Empirical Study for Comparing the Usability of Concurrent Programming Languages, in ESEM 2011 (ACM/IEEE International Symposium on Empirical Software Engineering and Measurement), 22-23 September 2011 (best paper award). Reports on a carefully designed empirical study to assess the teachability of various approaches to concurrent programming. Available here.

8. Martin Nordio, H.-Christian Estler, Julian Tschannen, Carlo Ghezzi, Elisabetta Di Nitto and Bertrand Meyer: How do Distribution and Time Zones affect Software Development? A Case Study on Communication, in Proceedings of the 6th International Conference on Global Software Engineering (ICGSE), IEEE Computer Press, 2011, pages 176-184. (A study of the results of our DOSE distributed course, which involved students from different universities in different countries collaborating on a common software development project.) Available here.

9. Martin Nordio, Carlo Ghezzi, Elisabetta Di Nitto, Giordano Tamburrelli, Julian Tschannen, Nazareno Aguirre, Vidya Kulkarni and Bertrand Meyer: Teaching Software Engineering using Globally Distributed Projects: the DOSE course, in Collaborative Teaching of Globally Distributed Software Development – Community Building Workshop (CTGDSD), Hawaii (at ICSE), May 2011. (Part of the experience of our Distributed Outsourced Software Engineering course, taught over many years with colleagues from Politecnico di Milano and elsewhere, see paper in previous entry.) Available here.

10. Bertrand Meyer: From Programming to Software Engineering (slides only), material for education keynote at International Conference on Software Engineering (ICSE 2010), Cape Town, South Africa, May 2010. Available here.

11. Michela Pedroni and Bertrand Meyer: Object-Oriented Modeling of Object-Oriented Concepts, in ISSEP 2010, Fourth International Conference on Informatics in Secondary Schools, Zurich, January 2010, eds. J. Hromkovic, R. Královic, J. Vahrenhold, Lecture Notes in Computer Science 5941, Springer, 2010. Available here.

12. Michela Pedroni, Manuel Oriol and Bertrand Meyer: What Do Beginning CS Majors Know?, ETH Technical Report, 2009. (Unpublished report about the background of 1st-year ETH CS students surveyed over many years. See shorter 2012 CACM version above.) Available here.

13. Bertrand Meyer: Touch of Class: Learning to Program Well Using Object Technology and Design by Contract, Springer, 2009 (also translated into Russian). (Introductory programming textbook, used for many years at ETH Zurich and Innopolis University for the first programming course. The herecontains a long discussion of pedagogical issues of teaching programming and CS.) Book page and text of several chapters here.

14. Michela Pedroni, Manuel Oriol, Lukas Angerer and Bertrand Meyer: Automatic Extraction of Notions from Course Material, in Proceedings of SIGCSE 2008 (39th Technical Symposium on Computer Science Education), Portland (Oregon), 12-15 March 2008, ACM SIGCSE Bulletin, vol. 40, no. 1, ACM Press, 2008, pages 251-255. (As the title indicates, tools for automatic analysis of course material to extract the key pedagogical notions or “Trucs”.) Available here.

15. Marie-Hélène Nienaltowski, Michela Pedroni and Bertrand Meyer: Compiler Error Messages: What Can Help Novices?, in Proceedings of SIGCSE 2008 (39th Technical Symposium on Computer Science Education), Portland (Oregon), Texas, 12-15 March 2008, ACM SIGCSE Bulletin, vol. 40, no. 1, ACM Press, 2008, pages 168-172. (Discusses the results of experiments with different styles of compiler error messages, which can be baffling to beginners, to determine what works best.) Available here.

16. Bertrand Meyer and Marco Piccioni: The Allure and Risks of a Deployable Software Engineering Project: Experiences with Both Local and Distributed Development, in Proceedings of IEEE Conference on Software Engineering & Training (CSEE&T), Charleston (South Carolina), 14-17 April 2008, ed. H. Saiedian, pages 3-16. (Paper associated with a keynote at an SE education conference. See other papers on the DOSE distributed project experience below.) Available here.

17. Marie-Hélène Ng Cheong Vee (Marie-Hélène Nienaltowski), Keith L. Mannock and Bertrand Meyer: Empirical study of novice error paths, Proceedings of workshop on educational data mining at the 8th international conference on intelligent tutoring systems (ITS 2006), 2006, pages 13-20. (An empirical study of the kind of programming mistakes learners make.) Available here.

18. Bertrand Meyer: Testable, Reusable Units of Cognition, in Computer (IEEE), vol. 39, no. 4, April 2006, pages 20-24. (Introduced a general approach for structuring knowledge for teaching purposes: “Trucs”. Served as the basis for some other work listed, in particular papers with Michela Pedroni on the topics of her PhD thesis. Available here.

19. Michela Pedroni and Bertrand Meyer: The Inverted Curriculum in Practice, in Proceedings of SIGCSE 2006, Houston (Texas), 1-5 March 2006, ACM Press, 2006, pages 481-485. (Develops the idea of inverted curriculum which served as the basis for our teaching of programming at ETH, Innopolis etc. and led to the “Touch of Class” textbook.) Available here.

20. Bertrand Meyer: The Outside-In Method of Teaching Introductory Programming, in Perspective of System Informatics, Proceedings of fifth Andrei Ershov Memorial Conference, Akademgorodok, Novosibirsk, 9-12 July 2003, eds. Manfred Broy and Alexandr Zamulin, Lecture Notes in Computer Science 2890, Springer, 2003, pages 66-78. (An early version of the ideas presented in the previous entry.) Available here.

21. Bertrand Meyer: Software Engineering in the Academy, in Computer (IEEE), vol. 34, no. 5, May 2001, pages 28-35. Translations: Russian in Otkrytye Systemy (Open Systems Publications), #07-08-2001, October 2001. (A general discussion of the fundamental concepts to be taught in software engineering. Served as a blueprint for my teaching at ETH.) Available here.

22. Bertrand Meyer: Object-Oriented Software Construction, second edition, Prentice Hall, 1296 pages, January 1997. Translations: Spanish, French Russian, Serbian, Japanese. (Not a publication on education per se but cited here since it is a textbook that has been widely used for teaching and has many comments on pedagogy.)
23. Bertrand Meyer: The Choice for Introductory Software Education, Guest editorial in Journal of Object-Oriented Programming, vol. 7, no. 3, June 1994, page 8. (A discussion of the use of Eiffel for teaching software engineering topics.)

24. Bertrand Meyer, Towards an Object-Oriented Curriculum, in Journal of Object-Oriented Programming, vo. 6, number 2, May 1993, pages 76-81. (Journal version of paper cited next.) Available here.

25. Bertrand Meyer: Towards an Object-Oriented Curriculum, in TOOLS 11, Technology of Object-Oriented Languages and Systems, Santa Barbara, August 1993, eds. Raimund Ege, Madhu Singh and B. Meyer, Prentice Hall 1993, pages 585-594. (Early advocacy for using OO techniques in teaching programming – while I was not in academia. Much of my subsequent educational work relied on those ideas.) Available here.

26. Bertrand Meyer: Object-Oriented Software Construction, Prentice Hall, 592 pages, 1988. (First edition, translated into German, Italian, French, Dutch, Romanian, Chinese. As noted for second edition above, not about education per se, but widely used textbook with pedagogical implications.)

27. Initiation à la programmation en milieu industriel (Teaching Modern Programming Methodology in an Industrial Environment), in RAIRO, série bleue (informatique), vol. 11, no. 1, pages 21-34 1977. (Early paper on teaching advanced programming techniques in industry.) Available here.

28. Claude Kaiser, Bertrand Meyer and Etienne Pichat, L’Enseignement de la Programmation à l’IIE (Teaching Programming at the IIE engineering school), in Zéro-Un Informatique, 1977. (A paper on my first teaching experience barely out of school myself.) Available here.

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A theorem of software engineering

Some of the folk wisdom going around in software engineering, often cluessly repeated for decades, is just wrong.  It can be particularly damaging when it affects key aspects of software development and is contradicted by solid scientific evidence. The present discussion covers a question that meets both of these conditions: whether it makes sense to add staff to a project to shorten its delivery time.

My aim is to popularize a result that is well known in the software engineering literature, going back to the early work of Barry Boehm [1], and explained with great clarity by Steve McConnell in his 2006 book on software cost estimation [2] under the name “Shortest Possible Schedule”. While an empirical rather than a logical result, I believe it deserves to be called a theorem (McConnell stays shy of using the term) because it is as close as we have in the area of software engineering management to a universal property, confirmed by numerous experimental studies.

This article contributes no new concept since McConnell’s chapter 20 says all there is to say about the topic;  my aim is simply to make the Shortest Possible Schedule Theorem better known, in particular to practitioners.

The myth about shortening project times begins with an observation that is clearly correct, at least in an extreme form. Everyone understands that if our project has been evaluated, through accepted cost estimation techniques, to require three developers over a year we cannot magically hire 36 people to complete it in one month. Productivity does not always scale up.

But neither does common sense. Too often the conclusion from the preceding trival observation takes the form of an old  saw, “Brooks’ Law”: adding people to a late project delays it further. The explanation is that the newcomers cost more through communication overhead than they bring through actual contributions. While a few other sayings of Brooks’ Mythical Man-Month have stood the test of time, this one has always struck me as describing, rather than any actual law, a definition of bad management. Of course if you keep haplessly throwing people at deadlines you are just going to add communication problems and make things worse. But if you are a competent manager expanding the team size is one of the tools at your disposal to improve the state of a project, and it would be foolish to deprive yourself of it. A definitive refutation of the supposed law, also by McConnell, was published 20 years ago [3].

For all the criticism it deserves, Brooks’s pronouncement was at least limited in its scope: it addressed addition of staff to a project that is already late. It is even wronger to apply it to the more general issue of cost-estimating and staffing software projects, at any stage of their progress.  Forty-year-old platitudes have even less weight here. As McConnell’s book shows, cost estimation is no longer a black art. It is not an exact science either, but techniques exist for producing solid estimates.

The Shortest Possible Schedule theorem is one of the most interesting results. Much more interesting than Brooks’s purported law, because it is backed by empirical studies (rather than asking us to believe one person’s pithy pronouncement), and instead of just a general negative view it provides a positive result complemented by a limitation of that result; and both are expressed quantitatively.

Figure 1 gives the general idea of the SPS theorem. General idea only; Figure 2 will provide a more precise view.

Image4

Figure 1: General view of the Shortest Possible Schedule theorem.

The  “nominal project” is the result of a cost and schedule estimation yielding the optimum point. The figure and the theorem provide project managers with both a reason to rejoice and a reason to despair:

  • Rejoice: by putting in more money, i.e. more people (in software engineering, project costs are essentially people costs [4]), you can bring the code to fruition faster.
  • Despair: whatever you do, there is a firm limit to the time you can gain: 25%. It seems to be a kind of universal constant of software engineering.

The “despair” part typically gets the most attention at first, since it sets an absolute value on how much money can buy (so to speak) in software: try as hard as you like, you will never get below 75% of the nominal (optimal) value. The “impossible zone” in Figure 1 expresses the fundamental limitation. This negative result is the reasoned and precise modern replacement for the older folk “law”.

The positive part, however, is just as important. A 75%-empty glass is also 25%-full. It may be disappointing for a project manager to realize that no amount of extra manpower will make it possible to guarantee to higher management more than a 25% reduction in time. But it is just as important to know that such a reduction, not at all insignificant, is in fact reachable given the right funding, the right people, the right tools and the right management skills. The last point is critical: money by itself does not suffice, you need management; Brooks’ law, as noted, is mostly an observation of the effects of bad management.

Figure 1 only carries the essential idea, and is not meant to provide precise numerical values. Figure 2, the original figure from McConnell’s book, is. It plots effort against time rather than the reverse but, more importantly, it shows several curves, each corresponding to a published empirical study or cost model surveyed by the book.

Image5

Figure 2: Original illustration of the Shortest Possible Schedule
(figure 2-20 of [3], reproduced with the author’s permission)

On the left of the nominal point, the curves show how, according to each study, increased cost leads to decreased time. They differ on the details: how much the project needs to spend, and which maximal reduction it can achieve. But they all agree on the basic Shortest Possible Schedule result: spending can decrease time, and the maximal reduction will not exceed 25%.

The figure also provides an answer, although a disappointing one, to another question that arises naturally. So far this discussion has assumed that time was the critical resource and that we were prepared to spend more to get a product out sooner. But sometimes it is the other way around: the critical resource is cost, or, concretely, the number of developers. Assume that nominal analysis tells us that the project will take four developers for a year and, correspondingly, cost 600K (choose your currency).  We only have a budget of 400K. Can we spend less by hiring fewer developers, accepting that it will take longer?

On that side, right of the nominal point in Figure 2, McConnell’s survey of surveys shows no consensus. Some studies and models do lead to decreased costs, others suggest that with the increase in time the cost will actually increase too. (Here is my interpretation, based on my experience rather than on any systematic study: you can indeed achieve the original goal with a somewhat smaller team over a longer period; but the effect on the final cost can vary. If the new time is t’= t + T and the new team size s’= s – S, t and s being the nominal values, the cost difference is proportional to  Ts – t’S. It can be positive as well as negative depending on the values of the original t and s and the precise effect of reduced team size on project duration.)

The firm result, however, is the left part of the figure. The Shortest Possible Schedule theorem confirms what good project managers know: you can, within limits, shorten delivery times by bringing all hands on deck. The precise version deserves to be widely known.

References and note

[1] Barry W. Boehm: Software Engineering Economics, Prentice Hall, 1981.

[2] Steve McConnell: Software Estimation ― Demystifying the Black Art, Microsoft Press, 2006.

[3] Steve McConnell: Brooks’ Law Repealed, in IEEE Software, vol. 16, no. 6, pp. 6–8, November-December 1999, available here.

[4] This is the accepted view, even though one might wish that the industry paid more attention to investment in tools in addition to people.

Recycled A version of this article was first published on the Comm. ACM blog under the title The Shortest Possible Schedule Theorem: Yes, You Can Throw Money at Software Deadlines

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Software Engineering Education: FISEE coming up

Over the past few days I have come across several people who told me they want to attend the Frontiers In Software Engineering Education (FISEE) workshop in Villebrumier, 11-13 November, but have not registered yet. If that’s your case please register right now because:

  • The number of spots is limited (it’s a residential event, everyone is hosted onsite, and there is a set number of rooms).
  • We need a preliminary program. The format of the event is flexible, Springer LNCS proceedings come after the meeting, we make room for impromptu presentations and discussions, but still we need a basic framework and we need to finalize it now.

So please go ahead and fill in the registration form.

From the previous posting about FISEE:

The next event at the LASER center in Villebrumier (Toulouse area, Southwest France) is FISEE, Frontiers in Software Engineering Education, see the web site. This small-scale workshop, 11 to 13 November is devoted to what Software Engineering needs, what should be changed, and how new and traditional institutions can adapt to the fast pace of technology.

Workshops at the Villebrumier center favor a friendly, informal and productive interaction between participants, who are all hosted on site. There are no formal submissions, but post-event proceedings will be published as part of the LASER sub-series of Springer Lecture Notes in Computer Science.

Like other events there, FISEE is by invitation; if you are active in the field of software engineering education as an educator, as a potential employer of software engineering graduates, or as a researcher, you can request an invitation by writing to me or one of the other organizers. Attendance is limited to 15-20 participants.

Among already scheduled talks: a keynote by Alexander Tormasov, rector of Innopolis University, and a talk by me on “the 15 concepts of software engineering”.

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Software engineering education: Villebrumier LASER center, November

The next event at the LASER center in Villebrumier (Toulouse area, Southwest France) is FISEE, Frontiers in Software Engineering Education, see the web site. This small-scale workshop, 11 to 13 November is devoted to what Software Engineering needs, what should be changed, and how new and traditional institutions can adapt to the fast pace of technology.

Workshops at the Villebrumier center favor a friendly, informal and productive interaction between participants, who are all hosted on site. There are no formal submissions, but post-event proceedings will be published as part of the LASER sub-series of Springer Lecture Notes in Computer Science.

Like other events there, FISEE is by invitation; if you are active in the field of software engineering education as an educator, as a potential employer of software engineering graduates, or as a researcher, you can request an invitation by writing to me or one of the other organizers. Attendance is limited to 15-20 participants.

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Sunrise was foggy today

Once you have learned the benefits of formally expressing requirements, you keep noticing potential ambiguities and other deficiencies [1] in everyday language. Most such cases are only worth a passing smile, but here’s one that perhaps can serve to illustrate a point with business analysts in your next requirements engineering workshop or with students in your next software engineering lecture.

As a customer of the Swiss telecommunications company Sunrise I receive an occasional “news” email. (As a customer of the Swiss telecommunications company Sunrise I would actually prefer that they spend my money improving  bandwidth,  but let us not digress.) Rather than raw marketing messages these are tips for everyday life, with the presumed intent of ingratiating the populace. For example, today’s message helpfully advises me on how to move house. The admirable advice starts (my translation):

10.7% of all Swiss people relocate every year. Is that your case too for next Autumn?

Actually no, it’s not my case (neither a case of being one of the “Swiss people” nor a case of intending to relocate this Fall). And, ah, the beauty of ridiculously precise statistics! Not 10.8% or 10.6%, mind you, no, 10.7% exactly! But consider the first sentence and think of something similar appearing in a requirements document or user story. Something similar does appear in such documents, all the time, leading to confusions for the programmers interpreting them and to bugs in the resulting systems. Those restless Swiss! Did you know that they include an itchy group, exactly 922,046 people (I will not be out-significant-digited!), who relocate every year?

Do not be silly, I hear you saying. What Sunrise is sharing of its wisdom is that every year a tenth of the Swiss population moves, but not the same tenth every year. Well, OK, maybe I am being silly. But if you think of a programmer reading such a statement about some unfamiliar domain (not one about which we can rely on common sense), the risk of confusion and consequent bugs is serious.

As [1] illustrated in detail, staying within the boundaries of natural language to resolve such possible ambiguities only results in convoluted requirements that make matters worse. The only practical way out is, for delicate system properties, to use precise language, also known technically as “mathematics”.

Here for example a precise formulation of the two possible interpretations removes any doubt. Let Swiss denote the set of Swiss people and  E the number of elements (cardinal) of a finite set E, which we can apply to the example because the set of Swiss people is indeed finite. Let us define slice as the Sunrise-official number of Swiss people relocating yearly, i.e. slice = Swiss ∗ 0.107 (the actual value appeared above). Then one interpretation of the fascinating Sunrise-official fact is:

{s: Swiss | (∀y: Year | s.is_moving (y))} = slice

In words: the cardinal of the set of Swiss people who move every year (i.e., such that for every year y they move during y) is equal to the size of the asserted population subset.

The other possible interpretation, the one we suspect would be officially preferred by the Sunrise powers (any formal-methods fan from Sunrise marketing reading this, please confirm or deny!), is:

∀y: Year | {s: Swiss | s.is_moving (y)} = slice

In words: for any year y, the cardinal of the set of Swiss people who move during y is equal to the size of the asserted subset.

This example is typical of where and why we need mathematics in software requirements. No absolutist stance here, no decree  that everything become formal (mathematical). Natural language is not going into retirement any time soon. But whenever one spots a possible ambiguity or imprecision, the immediate reaction should always be to express the concepts mathematically.

To anyone who has had a successful exposure to formal methods this reaction is automatic. But I keep getting astounded not only by  the total lack of awareness of these simple ideas among the overwhelming majority of software professionals, but also by their absence from the standard curriculum of even top universities. Most students graduate in computer science without ever having heard such a discussion. Where a formal methods course does exist, it is generally as a specialized topic reserved for a small minority, disconnected (as Leslie Lamport has observed [2]) from the standard teaching of programming and software engineering.

In fact all software engineers should possess the ability to go formal when and where needed. That skill is not hard to learn and should be practiced as part of the standard curriculum. Otherwise we keep training the equivalent of electricians rather than electrical engineers, programmers keep making damaging mistakes from misunderstanding ambiguous or inconsistent requirements, and we all keep suffering from buggy programs.

 

References

[1] Self-citation appropriate here: Bertrand Meyer: On Formalism in Specifications, IEEE Software, vol. 3, no. 1, January 1985, pages 6-25, available here.

[2] Leslie Lamport: The Future of Computing: Logic or Biology, text of a talk given at Christian Albrechts University, Kiel on 11 July 2003, available here.

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Schedule and last deadline for LASER AI + ML + SE, Elba, June

The lecture schedule has now been posted for the 2019 LASER summer school on artificial intelligence, machine learning and software engineering. The speakers are Shai Ben-David (Waterloo), Lionel Briand (Luxembourg), Pascal Fua (EPFL), Erik Meijer (Facebook), Tim Menzies (NC State) and I.

The last deadline for registration is May 20.

The school takes place June 1-9 in the magnificent Hotel del Golfo in Elba Island, Italy.

All details at www.laser-foundation.org/school/2019.

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Soundness and completeness: with precision

Over breakfast at your hotel you read an article berating banks about the fraudulent credit card transactions they let through. You proceed to check out and bang! Your credit card is rejected because (as you find out later) the bank thought [1] it couldn’t possibly be you in that exotic place. Ah, those banks! They accept too much. Ah, those banks! They reject too much. Finding the right balance is a case of soundness versus precision.

Similar notions are essential to the design of tools for program analysis, looking for such suspicious cases as  dead code (program parts that will never be executed). An analysis can be sound, or not; it can be complete, or not.

These widely used concepts are sometimes misunderstood.  The first answer I get when innocently asking people whether the concepts are clear is yes, of course, everyone knows! Then, as I bring up such examples as credit card rejection or dead code detection, assurance quickly yields to confusion. One sign that things are not going well is when people start throwing in terms like “true positive” and “false negative”. By then any prospect of reaching a clear conclusion has vanished. I hope that after reading this article you will never again (in a program analysis context) be tempted to use them.

Now the basic idea is simple. An analysis is sound if it reports all errors, and complete if it only reports errors. If not complete, it is all the more precise that it reports fewer non-errors.

You can stop here and not be too far off [2]. But a more nuanced and precise discussion helps.

1. A relative notion

As an example of common confusion, one often encounters attempts to help through something like Figure 1, which cannot be right since it implies that all sound methods are complete. (We’ll have better pictures below.)

Figure 1: Naïve (and wrong) illustration

Perhaps this example can be dismissed as just a bad use of illustrations [3] but consider the example of looking for dead code. If the analysis wrongly determines that some reachable code is unreachable, is it unsound or incomplete?

With this statement of the question, the only answer is: it depends!

It depends on the analyzer’s mandate:

  • If it is a code checker that alerts programmers to cases of bad programming style, it is incomplete: it reports as an error a case that is not. (Reporting that unreachable code is reachable would cause unsoundness, by missing a case that it should have reported.)
  • If it is the dead-code-removal algorithm of an optimizing compiler, which will remove unreachable code, it is unsound: the compiler will remove code that it should not. (Reporting that unreachable code is reachable would cause incompleteness, by depriving the compiler of an optimization.)

As another example, consider an analyzer that finds out whether a program will terminate. (If you are thinking “but that can’t be done!“, see the section “Appendix: about termination” at the very end of this article.) If it says a program does not terminates when in fact it does, is it unsound or incomplete?

Again, that depends on what the analyzer seeks to establish. If it is about the correctness of a plain input-to-output program (a program that produces results and then is done), we get incompleteness: the analyzer wrongly flags a program that is actually OK. But if it is about verifying that continuously running programs, such as the control system for a factory, will not stop (“liveness”), then the analyzer is unsound.

Examples are not limited to program analysis. A fraud-indentification process that occasionally rejects a legitimate credit card purchase is, from the viewpoint of preserving the bank from fraudulent purchases, incomplete. From the viewpoint of the customer who understands a credit card as an instrument enabling payments as long as you have sufficient credit, it is unsound.

These examples suffice to show that there cannot be absolute definitions of soundness and precision: the determination depends on which version of a boolean property we consider desirable. This decision is human and subjective. Dead code is desirable for the optimizing compiler and undesirable (we will say it is a violation) for the style checker. Termination is desirable for input-output programs and a violation for continuously running programs.

Once we have decided which cases are desirable and which are violations, we can define the concepts without any ambiguity: soundness means rejecting all violations, and completeness means accepting all desirables.

While this definition is in line with the unpretentious, informal one in the introduction, it makes two critical aspects explicit:

  • Relativity. Everything depends on an explicit decision of what is desirable and what is a violation. Do you want customers always to be able to use their credit cards for legitimate purchases, or do you want to detect all frauds attempts?
  • Duality. If you reverse the definitions of desirable and violation (they are the negation of each other), you automatically reverse the concepts of soundness and completeness and the associated properties.

We will now explore the consequences of these observations.

2. Theory and practice

For all sufficiently interesting problems, theoretical limits (known as Rice’s theorem) ensure that it is impossible to obtain both soundness and completeness.

But it is not good enough to say “we must be ready to renounce either soundness or completeness”. After all, it is very easy to obtain soundness if we forsake completeness: reject every case. A termination-enforcement analyzer can reject every program as potentially non-terminating. A bank that is concerned with fraud can reject every transaction (this seems to be my bank’s approach when I am traveling) as potentially fraudulent. Dually, it is easy to ensure completeness if we just sacrifice soundness: accept every case.

These extreme theoretical solutions are useless in practice; here we need to temper the theory with considerations of an engineering nature.

The practical situation is not as symmetric as the concept of duality theoretically suggests. If we have to sacrifice one of the two goals, it is generally better to accept some incompleteness: getting false alarms (spurious reports about cases that turn out to be harmless) is less damaging than missing errors. Soundness, in other words, is essential.

Even on the soundness side, though, practice tempers principle. We have to take into account the engineering reality of how tools get produced. Take a program analyzer. In principle it should cover the entire programming language. In practice, it will be built step by step: initially, it may not handle advanced features such as exceptions, or dynamic mechanisms such as reflection (a particularly hard nut to crack). So we may have to trade soundness for what has been called  “soundiness[4], meaning soundness outside of cases that the technology cannot handle yet.

If practical considerations lead us to more tolerance on the soundness side, on the completeness side they drag us (duality strikes again) in the opposite direction. Authors of analysis tools have much less flexibility than the theory would suggest. Actually, close to none. In principle, as noted, false alarms do not cause catastrophes, as missed violations do; but in practice they can be almost as bad.  Anyone who has ever worked on or with a static analyzer, going back to the venerable Lint analyzer for C, knows the golden rule: false alarms kill an analyzer. When people discover the tool and run it for the first time, they are thrilled to discover how it spots some harmful pattern in their program. What counts is what happens in subsequent runs. If the useful gems among the analyzer’s diagnostics are lost in a flood of irrelevant warnings, forget about the tool. People just do not have the patience to sift through the results. In practice any analysis tool has to be darn close to completeness if it has to stand any chance of adoption.

Completeness, the absence of false alarms, is an all-or-nothing property. Since in the general case we cannot achieve it if we also want soundness, the engineering approach suggests using a numerical rather than boolean criterion: precision. We may define the precision pr as 1 – im where im is the imprecision:  the proportion of false alarms.

The theory of classification defines precision differently: as pr = tp / (tp + fp), where tp is the number of false positives and fp the number of true positives. (Then im would be fp / (tp + fp).) We will come back to this definition, which requires some tuning for program analyzers.

From classification theory also comes the notion of recall: tp / (tp + fn) where fn is the number of false negatives. In the kind of application that we are looking at, recall corresponds to soundness, taken not as a boolean property (“is my program sound?“) but  a quantitative one (“how sound is my program?“). The degree of unsoundness un would then be fn / (tp + fn).

3. Rigorous definitions

With the benefit of the preceding definitions, we can illustrate the concepts, correctly this time. Figure 2 shows two different divisions of the set of U of call cases (universe):

  • Some cases are desirable (D) and others are violations (V).
  • We would like to know which are which, but we have no way of finding out the exact answer, so instead we run an analysis which passes some cases (P) and rejects some others (R).

Figure 2: All cases, classified

The first classification, left versus right columns in Figure 2, is how things are (the reality). The second classification, top versus bottom rows, is how we try to assess them. Then we get four possible categories:

  • In two categories, marked in green, assessment hits reality on the nail:  accepted desirables (A), rightly passed, and caught violations (C), rightly rejected.
  • In the other two, marked in red, the assessment is off the mark: missed violations (M), wrongly passed; and false alarms (F), wrongly accepted.

The following properties hold, where U (Universe) is the set of all cases and  ⊕ is disjoint union [5]:

— Properties applicable to all cases:
U = D ⊕ V
U = P ⊕ R
D = A ⊕ F
V = C ⊕ M
P = A ⊕ M
R = C ⊕ F
U = A ⊕M ⊕ F ⊕ C

We also see how to define the precision pr: as the proportion of actual violations to reported violations, that is, the size of C relative to R. With the convention that u is the size of U and so on, then  pr = c / r, that is to say:

  • pr = c / (c + f)      — Precision
  • im = f / (c + f)      — Imprecision

We can similarly define soundness in its quantitative variant (recall):

  • so = a / (a + m)      — Soundness (quantitative)
  • un = m / (a + m)   — Unsoundness

These properties reflect the full duality of soundness and completeness. If we reverse our (subjective) criterion of what makes a case desirable or a violation, everything else gets swapped too, as follows:

Figure 3: Duality

We will say that properties paired this way “dual” each other [6].

It is just as important (perhaps as a symptom that things are not as obvious as sometimes assumed) to note which properties do not dual. The most important examples are the concepts of  “true” and “false” as used in “true positive” etc. These expressions are all the more confusing that the concepts of True and False do dual each other in the standard duality of Boolean algebra (where True duals False,  Or duals And, and an expression duals its negation). In “true positive” or “false negative”,  “true” and “false” do not mean True and False: they mean cases in which (see figure 2 again) the assessment respectively matches or does not match the reality. Under duality we reverse the criteria in both the reality and the assessment; but matching remains matching! The green areas remain green and the red areas remain red.

The dual of positive is negative, but the dual of true is true and the dual of false is false (in the sense in which those terms are used here: matching or not). So the dual of true positive is true negative, not false negative, and so on. Hereby lies the source of the endless confusions.

The terminology of this article removes these confusions. Desirable duals violation, passed duals rejected, the green areas dual each other and the red areas dual each other.

4. Sound and complete analyses

If we define an ideal world as one in which assessment matches reality [7], then figure 2 would simplify to just two possibilities, the green areas:

Figure 4: Perfect analysis (sound and complete)

This scheme has the following properties:

— Properties of a perfect (sound and complete) analysis as in Figure 4:
M = ∅              — No missed violations
F = ∅               — No false alarms
P = D                — Identify  desirables exactly
R = V                –Identify violations exactly

As we have seen, however, the perfect analysis is usually impossible. We can choose to build a sound solution, potentially incomplete:

Figure 5: Sound desirability analysis, not complete

In this case:

— Properties of a sound analysis (not necessarily complete) as in Figure 5:
M = ∅              — No missed violations
P = A                — Accept only desirables
V = C                — Catch all violations
P ⊆ D               — Under-approximate desirables
R ⊇ V               — Over-approximate violations

Note the last two properties. In the perfect solution, the properties P = D and R = V mean that the assessment, yielding P and V, exactly matches the reality, D and V. From now on we settle for assessments that approximate the sets of interest: under-approximations, where the assessment is guaranteed to compute no more than the reality, and over-approximations, where it computes no less. In all cases the assessed sets are either subsets or supersets of their counterparts. (Non-strict, i.e. ⊆ and ⊇ rather than ⊂ and ⊃; “approximation” means possible approximation. We may on occasion be lucky and capture reality exactly.)

We can go dual and reach for completeness at the price of possible unsoundness:

Figure 6: Complete desirability analysis, not sound

The properties are dualled too:

— Properties of a complete analysis (not necessarily sound), as in Figure 6:
F = ∅              — No false alarms
R = C               — Reject only violations
D = A               — Accept all desirables
P ⊇ D               — Over-approximate desirables
R ⊆ V              — Under-approximate violations

5. Desirability analysis versus violation analysis

We saw above why the terms “true positives”, “false negatives” etc., which do not cause any qualms in classification theory, are deceptive when applied to the kind of pass/fail analysis (desirables versus violations) of interest here. The definition of precision provides further evidence of the damage. Figure 7 takes us back to the general case of Figure 2 (for analysis that is guaranteed neither sound nor complete)  but adds these terms to the respective categories.

Figure 7: Desirability analysis (same as fig. 2 with added labeling)

The analyzer checks for a certain desirable property, so if it wrongly reports a violation (F) that is a false negative, and if it misses a violation (M) it is a false positive. In the  definition from classification theory (section 2, with abbreviations standing for True/False Positives/Negatives): TP = A, FP = M, FN =  F, TN = C, and similarly for the set sizes: tp = a, fp = m, fn = f, tn = c.

The definition of precision from classification theory was pr = tp / (tp + fp), which here gives a / (a + m). This cannot be right! Precision has to do with how close the analysis is to completeness, that is to day, catching all violations.

Is classification theory wrong? Of course not. It is simply that, just as Alice stepped on the wrong side of the mirror, we stepped on the wrong side of duality. Figures 2 and 7 describe desirability analysis: checking that a tool does something good. We assess non-fraud from the bank’s viewpoint, not the stranded customer’s; termination of input-to-output programs, not continuously running ones; code reachability for a static checker, not an optimizing compiler. Then, as seen in section 3, a / (a + m) describes not precision but  soundness (in its quantitative interpretation, the parameter called “so” above).

To restore the link with classification theory , we simply have to go dual and take the viewpoint of violation analysis. If we are looking for possible violations, the picture looks like this:

Figure 8: Violation analysis (same as fig. 7 with different positive/negative labeling)

Then everything falls into place:  tp = c, fp = f, fn =  m, tn = a, and the classical definition of  precision as pr = tp / (tp + fp) yields c / (c + f) as we are entitled to expect.

In truth there should have been no confusion since we always have the same picture, going back to Figure 2, which accurately covers all cases and supports both interpretations: desirability analysis and violation analysis. The confusion, as noted, comes from using the duality-resistant “true”/”false” opposition.

To avoid such needless confusion, we should use the four categories of the present discussion:  accepted desirables, false alarms, caught violations and missed violations [8]. Figure 2 and its variants clearly show the duality, given explicitly in Figure 3, and sustains  interpretations both for desirability analysis and for violation analysis. Soundness and completeness are simply special cases of the general framework, obtained by ruling out one of the cases of incorrect analysis in each of Figures 4 and 5. The set-theoretical properties listed after Figure 2 express the key concepts and remain applicable in all variants. Precision c / (c + f) and quantitative soundness a / (a + m) have unambiguous definitions matching intuition.

The discussion is, I hope, sound. I have tried to make it complete. Well, at least it is precise.

Notes and references

[1] Actually it’s not your bank that “thinks” so but its wonderful new “Artificial Intelligence” program.

[2] For a discussion of these concepts as used in testing see Mauro Pezzè and Michal Young, Software Testing and Analysis: Process, Principles and Techniques, Wiley, 2008.

[3] Edward E. Tufte: The Visual Display of Quantitative Information, 2nd edition, Graphics Press, 2001.

[4] Michael Hicks,What is soundness (in static analysis)?, blog article available here, October 2017.

[5] The disjoint union property X = Y ⊕ Z means that Y ∩ Z = ∅ (Y and Z are disjoint) and X = Y ∪ Z (together, they yield X).

[6] I thought this article would mark the introduction into the English language of “dual” as a verb, but no, it already exists in the sense of turning a road from one-lane to two-lane (dual).

[7] As immortalized in a toast from the cult movie The Prisoner of the Caucasus: “My great-grandfather says: I have the desire to buy a house, but I do not have the possibility. I have the possibility to buy a goat, but I do not have the desire. So let us drink to the matching of our desires with our possibilities.” See 6:52 in the version with English subtitles.

[8] To be fully consistent we should replace the term “false alarm” by rejected desirable. I is have retained it because it is so well established and, with the rest of the terminology as presented, does not cause confusion.

[9] Byron Cook, Andreas Podelski, Andrey Rybalchenko: Proving Program Termination, in Communications of the ACM, May 2011, Vol. 54 No. 5, Pages 88-98.

Background and acknowledgments

This reflection arose from ongoing work on static analysis of OO structures, when I needed to write formal proofs of soundness and completeness and found that the definitions of these concepts are more subtle than commonly assumed. I almost renounced writing the present article when I saw Michael Hicks’s contribution [4]; it is illuminating, but I felt there was still something to add. For example, Hicks’s set-based illustration is correct but still in my opinion too complex; I believe that the simple 2 x 2 pictures used above convey the ideas  more clearly. On substance, his presentation and others that I have seen do not explicitly mention duality, which in my view is the key concept at work here.

I am grateful to Carlo Ghezzi for enlightening discussions, and benefited from comments by Alexandr Naumchev and others from the Software Engineering Laboratory at Innopolis University.

Appendix: about termination

With apologies to readers who have known all of the following from kindergarten: a statement such as (section 1): “consider an analyzer that finds out whether a program will terminate” can elicit no particular reaction (the enviable bliss of ignorance) or the shocked rejoinder that such an analyzer is impossible because termination (the “halting” problem) is undecidable. This reaction is just as incorrect as the first. The undecidability result for the halting problem says that it is impossible to write a general termination analyzer that will always provide the right answer, in the sense of both soundness and completeness, for any program in a realistic programming language. But that does not preclude writing termination analyzers that answer the question correctly, in finite time, for given programs. After all it is not hard to write an analyzer that will tell us that the program from do_nothing until True loop do_nothing end will terminate and that the program from do_nothing until False loop do_nothing end will not terminate. In the practice of software verification today, analyzers can give such sound answers for very large classes of programs, particularly with some help from programmers who can obligingly provide variants (loop variants, recursion variants). For a look into the state of the art on termination, see the beautiful survey by Cook, Podelski and Rybalchenko [9].

Also appears in the Communications of the ACM blog

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Gail Murphy to speak at Devops 19

The DEVOPS 2019 workshop (6-8 May 2019) follows a first 2018 workshop whose proceedings [1] have just been published in the special LASER-Villebrumier subseries of Springer Lecture notes in Computer Science. It is devoted to software engineering aspects of continuous development and new paradigms of software production and deployment, including but not limited to DevOps.

The keynote will be delivered by Gail Murphy, vice-president Research & Innovation at University of British Columbia and one of leaders in the field of empirical software engineering.

The workshop is held at the LASER conference center in Villebrumier near Toulouse. It is by invitation; if you would like to receive an invitation please contact one of the organizers (Jean-Michel Bruel, Manuel Mazzara and me) with a short description of your interest in the field.

Reference

Jean-Michel Bruel, Manuel Mazzara and Bertrand Meyer (eds.), Software Engineering Aspects of Continuous Development and New Paradigms of Software Production and Deployment, First International Workshop, DEVOPS 2018, Chateau de Villebrumier, France, March 5-6, 2018, Revised Selected Papers, see here..

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AI+ML+SE — Reminder about LASER school, coming up in June

A reminder about this year’s LASER school, taking place in Elba, Italy, June 1 to 9. The theme is

               AI + ML + SE

and the speakers:

  • Shai Ben-David, University of Waterloo
  • Lionel C. Briand, University of Luxembourg
  • Pascal Fua, EPFL
  • Eric Meijer, Facebook
  • Tim Menzies, NC State University
  • Me

Details at https://www.laser-foundation.org/school/.  From that page:

The 15th edition of the prestigious LASER summer school, in the first week of June 2019, will be devoted to the complementarity and confluence of three major areas of innovation in IT: Artificial Intelligence, Machine Learning and of course Software Engineering.

The school takes place in the outstanding environment of the Hotel del Golfo in Procchio, Elba, off the coast of Tuscany.

 

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The Formal Picnic approach to requirements

picnicRequirements engineering (studying and documenting what a software system should do, independently of how it will do it) took some time to be recognized as a key part of software engineering, since the early focus was, understandably, on programming. It is today a recognized sub-discipline and has benefited in the last decades from many seminal concepts. An early paper of mine, On Formalism in Specifications [1], came at the beginning of this evolution; it made the case for using formal (mathematics-based) approaches. One of the reasons it attracted attention is its analysis of the “seven sins of the specifier”: a list of pitfalls into which authors of specifications and requirements commonly fall.

One of the techniques presented in the paper has not made it into the standard requirements-enginering bag of tricks. I think it deserves to be known, hence the present note. There really will not be anything here that is not in the original article; in fact I will be so lazy as to reuse its example. (Current requirements research with colleagues should lead to the publication of new examples.)

Maybe the reason the idea did not register is that I did not give it a name. So here goes: formal picnic.

The usual software engineering curriculum includes, regrettably, no room for  field trips. We are jealous of students and teachers of geology or zoology and their occasional excursions: once in a while you put on your boots, harness your backpack, and head out to quarries or grasslands to watch pebbles or critters in flagrante, after a long walk with the other boys and girls and before all having lunch together in the wild. Yes, scientific life in these disciplines really is a picnic. What I propose for the requirements process is a similar excursion; not into muddy fields, but into the dry pastures of mathematics.

The mathematical picnic process starts with a natural-language requirements document. It continues, for some part of the requirements, with a translation into a mathematical version. It terminates with a return trip into natural language.

The formal approach to requirements, based on mathematical notations (as was discussed in my paper), is still controversial; a common objection is that requirements must be understandable by ordinary project stakeholders, many of whom do not have advanced mathematical skills. I am not entering this debate here, but there can be little doubt that delicate system properties can be a useful step, if only for the requirements engineers themselves. Mathematical notation forces precision.

What, then, if we want to end up with natural language for clarity, but also to take advantage of the precision of mathematics? The formal picnic answer is that we can use mathematics as a tool to improve the requirements. The three steps are:

  • Start: a natural-language requirements document. Typically too vague and deficient in other ways (the seven sins) to serve as an adequate basis for the rest of the software process, as a good requirements document should.
  • Picnic: an excursion into mathematics. One of the main purposes of a requirements process is to raise and answer key questions about the system’s properties. Using mathematics helps raise the right questions and obtain precise answers. You do not need to apply the mathematical picnic to the entire system: even if the overall specification remains informal, some particularly delicate aspects may benefit from a more rigorous analysis.
  • Return trip: thinking of the non-formalist stakeholders back home, we translate the mathematical descriptions into a new natural-language version.

This final version is still in (say) English, but typically not the kind of English that most people naturally write. It may in fact “sound funny”. That is because it is really just mathematical formulae translated back into English. It retains the precision and objectivity of mathematics, but is expressed in terms that anyone can understand.

Let me illustrate the mathematical picnic idea with the example from my article. For reasons that do not need to be repeated here (they are in the original), it discussed a very elementary problem of text processing: splitting a text across lines. The original statement of the problem, from a paper by Peter Naur, read:

Given a text consisting of words separated by BLANKS or by NL (new line) characters, convert it to a line-by-line form in accordance with the following rules: (1) line breaks must be made only where the given text has BLANK or NL; (2) each line is filled as far as possible as long as  (3) no line will contain more than MAXPOS characters.

My article then cited an alternative specification proposed in a paper by testing experts John Goodenough and Susan Gerhart. G&G criticized Naur’s work (part of the still relevant debate between proponents of tests and proponents of proofs such as Naur). They pointed out deficiencies in his simple problem statement above; for example, it says nothing about the case of a text containing a word of more than MAXPOS characters. G&G stated that the issue was largely one of specification (requirements) and went on to propose a new problem description, four times as long as Naur’s. In my own article, I had a field day taking aim at their own endeavor. (Sometime later I met Susan Gerhart, who was incredibly gracious about my critique of her work, and became an esteemed colleague.) I am not going to cite the G&G replacement specification here; you can find it in my article.

Since that article’s topic was formal approaches, it provided a mathematical statement of Naur’s problem. It noted that  the benefit of mathematical formalization is not just to gain precision but also to identify important questions about the problem, with a view to rooting out dangerous potential bugs. Mathematics means not just formalization but proofs. If you formalize the Naur problem, you soon realize that — as originally posed — it does not always have a solution (because of over-MAXPOS words). The process forces you to specify the conditions under which solutions do exist. This is one of the software engineering benefits of a mathematical formalization effort: if such conditions are not identified at the requirements level, they will take their revenge in the program, in the form of erroneous results and crashes.

You can find the mathematical specification (only one of several possibilities) in the article.  The discussion also noted that one could start again from that spec and go back to English. That was, without the name, the mathematical picnic. The result’s length is in-between the other two versions: twice Naur’s, but half G&G’s. Here it is:

Given are a non-negative integer MAXPOS and a character set including two “break characters” blank and newline. The program shall accept as input a finite sequence of characters and produce as output a sequence of characters satisfying the following conditions:
• It only differs from the input by having a single break character wherever the input has one or more break characters;
• Any MAXPOS + 1 consecutive characters include a newline;
• The number of newline characters is minimal.
If (and only if) an input sequence contains a group of MAXPOS + 1 consecutive nonbreak characters, there exists no such output. In this case, the program shall produce the output associated with the initial part of the sequence, up to and including the MAXPOS·th character of the first such group, and report the error.

This post-picnic version is the result of a quasi-mechanical retranscription from the mathematical specification in the paper.

It uses the kind of English that one gets after a mathematical excursion. I wrote above that this style might sound funny; not to me in fact, because I am used to mathematical picnics, but probably to others (does it sound funny to you?).

The picnic technique provides a good combination of the precision of mathematics and the readability of English. English requirements as ordinarily written are subject to the seven sins described in my article, from ambiguity and contradiction to overspecification and noise. A formalization effort can correct these issues, but yields a mathematical text. Whether we like it or not, many people react negatively to such texts. We might wish they learn, but that is often not an option, and if they are important stakeholders we need their endorsement or correction of the requirements. With a mathematical picnic we translate the formal text back into something they will understand, while avoiding the worst problems of natural-language specifications.

Practicing the Formal Picnic method also has a long-term benefit for a software team. Having seen first-hand that better natural-language specifications (noise-free and more precise) are possible, team members little by little learn to apply the same style to the English texts they write, even without a mathematical detour.

If the goal is high-quality requirements, is there any alternative? What I have seen in many requirements documents is a fearful attempt to avoid ambiguity and imprecision by leaving no stone unturned: adding information and redundancy over and again. This was very much what I criticized in the G&G statement of requirements, which attempted to correct the deficiencies of the Naur text by throwing ever-more details that caused ever more risks of entanglement. It is fascinating to see how every explanation added in the hope of filling a possible gap creates more sources of potential confusion and a need for even more explanations. In industrial projects, this is the process that leads to thousands-of-pages documents, so formidable that they end up (as in the famous Ariane-5 case) on a shelf where no one will consult them when they would provide critical answers.

Mathematical specifications yield the precision and uncover the contradictions, but they also avoid noise and remain terse. Translating them back into English yields a reasonable tradeoff. Try a formal picnic one of these days.

Acknowledgments

For numerous recent discussions of these and many other related topics, I am grateful to my colleagues from the Innopolis-Toulouse requirements research group: Jean-Michel Bruel, Sophie Ebersold, Florian Galinier, Manuel Mazzara and Alexander Naumchev. I remain grateful to Axel van Lamsweerde (beyond his own seminal contributions to requirements engineering) for telling me, six years after I published a version of [1] in French, that I should take the time to produce a version in English too.

Reference

Bertrand Meyer: On Formalism in Specifications, in IEEE Software, vol. 3, no. 1, January 1985, pages 6-25. PDF available via IEEE Xplore with account, and also from here. Adapted translation of an original article in French (AFCET Software Engineering newsletter, no. 1, pages 81-122, 1979).

(This article was originally published on the Comm. ACMM blog.)

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I didn’t make it up…

An article published here a few years ago, reproducing a note I wrote much earlier (1992), pointed out that conventional wisdom about the history of software engineering, cited in every textbook, is inaccurate: the term “software engineering” was in use before the famous 1968 Garmisch-Partenkichen conference. See that article for details.

Recently a colleague wanted to cite my observation but could not find my source, a 1966 Communications of the ACM article using the term. Indeed that text is not currently part of the digitalized ACM archive (Digital Library). But I knew it was not a figment of my imagination or of a bad memory.

The reference given in my note is indeed correct; with the help of the ETH library, I was able to get a scan of the original printed article. It is available here.

The text is not a regular CACM article but a president’s letter, part of the magazine’s front matter, which the digital record does not always include. In this case historical interest suggests it should; I have asked the ACM to add it. In the meantime, you can read the scanned version for  a nostalgic peek into what the profession found interesting half a century ago.

Note (12 November 2018): The ACM Digital Library responded (in a matter of hours!) and added the letter to the digital archive.

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Empirical answers: keynote and deadline

The EAQSE workshop is devoted to Empirical Answers to Questions of Software Engineering. The deadline is looming. Just announced: Tom Zimmerman from Microsoft Research will deliver the keynote. Tom is uniquely qualified; he has performed unique studies of what people, particularly software engineering practitioners, expect from empirical research. See hereand there. Outstanding papers.

See also the list of candidate questions on the workshop site.

The background for the workshop is the observation, stated in two earlier articles in this blog (starting here), that empirical studies of software engineering have made tremendous progress over the past decades, and that it is important to move the focus to what is important for practicing software developers, not just what can be studied (the lamppost temptation).

As to the submissions: if anyone needs a little more time, write to the organizers, we should be able to accommodate a few more days (but only a few). The proceedings will be published (in LNCS) after the event,  so the focus for the meeting itself is on presentation of important ideas.

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More French Poetry on Amazon

Involuntary poetry that is. This one is even more puzzling, in its own charming way, than the previously cited example.

From https://www.amazon.fr/dp/B072V71WVN/ref=psdc_3155122031_t4_B073PYSZNG:

Pour être une dame ou un monsieur : Bouteille de vin automatique ouverte sans effort avec ce tire-bouchon électrique. Gardez votre élégant ou votre gentleman pendant que vous ouvrez la bouteille de vin. Pas de problème. Tu es une dame mais tu es aussi un homme, Vous en aurez besoin et cela vous permettra de garder votre élégant ou monsieur.

Finalement j’ai préféré garder mon élégant.

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Why not program right?

recycled-logo (Originally published on CACM blog.)

Most of the world programs in a very strange way. Strange to me. I usually hear the reverse question: people ask us, the Eiffel community, to explain why we program our way. I hardly understand the question, because the only mystery is how anyone can even program in any other way.

The natural reference is the beginning of One Flew Over the Cuckoo’s Nest: when entering an insane asylum and wondering who is an inmate and who a doctor, you may feel at a loss for objective criteria. Maybe the rest of the world is right and we are the nut cases. Common sense suggests it.

But sometimes one can go beyond common sense and examine the evidence. So lend me an ear while I explain my latest class invariant. Here it is, in Figure 1. (Wait, do not just run away yet.)

multigraph_invariant

Figure 1: From the invariant of class MULTIGRAPH

This is a program in progress and by the time you read this note the invariant and enclosing class will have changed. But the ideas will remain.

Context: multigraphs

The class is called MULTIGRAPH and describes a generalized notion of graph, illustrated in Figure 2. The differences are that: there can be more than one edge between two nodes, as long as they have different tags (like the spouse and boss edges between 1 and 2); and there can be more than one edge coming out of a given node and with a given tag (such as the two boss edges out of 1, reflecting that 1’s boss might be 2 in some cases and 3 in others). Some of the nodes, just 1 here, are “roots”.

The class implements the notion of multigraph and provides a wide range of operations on multigraphs.

multigraph_example

Figure 2: A multigraph

Data structures

Now we turn to the programming and software engineering aspects. I am playing with various ways of accessing multigraphs. For the basic representation of a multigraph, I have chosen a table of triples:

                triples_table: HASH_TABLE [TRIPLE, TUPLE [source: INTEGER; tag: INTEGER; target: INTEGER]]  — Table of triples, each retrievable through its `source’, `tag’ and `target’.

where the class TRIPLE describes [source, tag, target] triples, with a few other properties, so they are not just tuples. It is convenient to use a hash table, where the key is such a 3-tuple. (In an earlier version I used just an ARRAY [TRIPLE], but a hash table proved more flexible.)

Sources and targets are nodes, also called “objects”; we represent both objects and tags by integers for efficiency. It is easy to have structures that map symbolic tag names such as “boss” to integers.

triples_table is the core data structure but it turns out that for the many needed operations it is convenient to have others. This technique is standard: for efficiency, provide different structures to access and manipulate the same underlying information, with some redundancy. So I also have:

 triples_from:  ARRAYED_LIST [LIST [TRIPLE]]
               — Triples starting from a given object. Indexed by object numbers.

  triples_with:  HASH_TABLE [LIST [TRIPLE], INTEGER]
               — Triples labeled by a given tag. Key is tag number.

 triples_to:  ARRAYED_LIST [LIST [TRIPLE]]
               — Triples leading into a given object. Indexed by object numbers.

Figure 3 illustrates triples_from and Figures 4 illustrates triples_with. triples_to is similar.

triples_from

Figure 3: The triples_from array of lists and the triples_table

triples_with

Figure 4: The triples_with array of lists and the triples_table

It is also useful to access multigraphs through yet another structure, which gives us the targets associated with a given object and tag:

successors: ARRAY [HASH_TABLE [LIST [TRIPLE], INTEGER]]
               — successors [obj] [t] includes all o such that there is a t- reference from obj to o.

For example in Figure 1 successors [1] [spouse] is {2, 3}, and in Figures 3 and 4 successors [26] [t] is {22, 55, 57}. Of course we can obtain the “successors” information through the previously defined structures, but since this is a frequently needed operation I decided to include a specific data structure (implying that every operation modifying the multigraph must update it). I can change my mind later on and decide to make “successors” a function rather than a data structure; it is part of the beauty of OO programming, particularly in Eiffel, that such changes are smooth and hardly impact client classes.

There is similar redundancy in representing roots:

                roots:  LINKED_SET [INTEGER]
                              — Objects that are roots.

                is_root:  ARRAY [BOOLEAN]
                              — Which objects are roots? Indexed by object numbers.

If o is a root, then it appears in the “roots” set and is_root [o] has value True.

Getting things right

These are my data structures. Providing such a variety of access modes is a common programming technique. From a software engineering perspective ― specification, implementation, verification… ― it courts disaster. How do we maintain their consistency? It is very easy for a small mistake to slip into an operation modifying the graph, causing one of the data structures to be improperly updated, but in a subtle and rare enough way that it will not manifest itself during testing, coming back later to cause strange behavior that will be very hard to debug.

For example, one of the reasons I have a class TRIPLE and not just 3-tuples is that a triple is not exactly  the same as an edge in the multigraph. I have decided that by default the operation that removes and edge would not remove the corresponding triple from the data structure, but leave it in and mark it as “inoperative” (so class TRIPLE has an extra “is_inoperative” boolean field). There is an explicit GC-like mechanism to clean up deleted edges occasionally. This approach brings efficiency but makes the setup more delicate since we have to be extremely careful about what a triple means and what removal means.

This is where I stop understanding how the rest of the world can work at all. Without some rigorous tools I just do not see how one can get such things right. Well, sure, spend weeks of trying out test cases, printing out the structures, manually check everything (in the testing world this is known as writing lots of “oracles”), try at great pains to find out the reason for wrong results, guess what program change will fix the problem, and start again. Stop when things look OK. When, as Tony Hoare once wrote, there are no obvious errors left.

Setting aside the minuscule share of projects (typically in embedded life-critical systems) that use some kind of formal verification, this process is what everyone practices. One can only marvel that systems, including many successful ones, get produced at all. To take an analogy from another discipline, this does not compare to working like an electrical engineer. It amounts to working like an electrician.

For a short time I programmed like that too (one has to start somewhere, and programming methodology was not taught back then). I no longer could today. Continuing with the Hoare citation, the only acceptable situation is to stop when there are obviously no errors left.

How? Certainly not, in my case, by always being right the first time. I make mistakes like everyone else does. But I have the methodology and tools to avoid some, and, for those that do slip through, to spot and fix them quickly.

Help is available

First, the type system. Lots of inconsistencies, some small and some huge, which in an untyped language would only hit during execution, do not make it past compilation. We are not just talking here about using REAL instead of INTEGER. With a sophisticated type system involving multiple inheritance, genericity, information hiding and void safety, a compiler error message can reflect a tricky logical mistake. You are using a SET as if it were a LIST (some operations are common, but others not). You are calling an operation on a reference that may be void (null) at run time. And so on.

By the way, about void-safety: for a decade now, Eiffel has been void-safe, meaning a compile-time guarantee of no run-time null pointer dereferencing. It is beyond my understanding how the rest of the world can still live with programs that run under myriad swords of Damocles: x.op (…) calls that might any minute, without any warning or precedent, hit a null x and crash.

Then there is the guarantee of logical consistency, which is where my class invariant (Figure 1) comes in. Maybe it scared you, but in reality it is all simple concepts, intended to make sure that you know what you are doing, and rely on tools to check that you are right. When you are writing your program, you are positing all kinds, logical assumptions, large and (mostly) small, all the time. Here, for the structure triples_from [o] to make sense, it must be a list such that:

  • It contains all the triples t in the triples_table such that t.source = o.
  •  It contains only those triples!

You know this when you write the program; otherwise you would not be having a “triples_from” structure. Such gems of knowledge should remain an integral part of the program. Individually they may not be rocket science, but accumulated over the lifetime of a class design, a subsystem design or a system design they collect all the intelligence that makes the software possible.  Yet in the standard process they are gone the next minute! (At best, some programmers may write a comment, but that does not happen very often, and a comment has no guarantee of precision and no effect on testing or correctness.)

Anyone who takes software development seriously must record such fundamental properties. Here we need the following invariant clause:

across triples_from as tf all

across tf.item as tp all tp.item.source = tf.cursor_index end

end

(It comes in the class, as shown in Figure 1, with the label “from_list_consistent”. Such labels are important for documentation and debugging purposes. We omit them here for brevity.)

What does that mean? If we could use Unicode (more precisely, if we could type it easily with our keyboards) we would write things like “∀ x: E | P (x) for all x in E, property P holds of x. We need programming-language syntax and write this as across E as x all P (x.item) end. The only subtlety is the .item part, which gives us generality beyond the  notation: x in the across is not an individual element of E but a cursor that moves over E. The actual element at cursor position is x.item, one of the properties of that cursor. The advantage is that the cursor has more properties, for example x.cursor_index, which gives its position in E. You do not get that with the plain of mathematics.

If instead of  you want  (there exists), use some instead of all. That is pretty much all you need to know to understand all the invariant clauses of class MULTIGRAPH as given in Figure 1.

So what the above invariant clause says is: take every position tf in triples_from; its position is tf.cursor_index and its value is tf.item. triples_from is declared as ARRAYED_LIST [LIST [TRIPLE]], so tf.cursor_index is an integer representing an object o, and tf.item is a list of triples. That list should  consist of the triples having tf.cursor_index as their source. This is the very property that we are expressing in this invariant clause, where the innermost across says: for every triple tp.item in the list, the source of that triple is the cursor index (of the outside across). Simple and straightforward, I think (although such English explanations are so much more verbose than formal versions, such as the Eiffel one here, and once you get the hang of it you will not need them any more).

How can one ever include a structure such as triples_from without expressing such a property? To put the question slightly differently: am I inside the asylum looking out, or outside the asylum looking in? Any clue would be greatly appreciated.

More properties

For the tag ( with_) and target lists, the properties are similar:

across triples_with as tw all across tw.item as tp all tp.item.tag = tw.key end end

across triples_to as tt all across tt.item as tp all tp.item.target = tt.cursor_index end end 

We also have some properties of array bounds:

 is_root.lower = 1 and is_root.upper = object_count

triples_from.lower = 1 and triples_from.upper = object_count

triples_to.lower = 1 and triples_to.upper = object_count

where object_count is the number of objects (nodes), and for an array a (whose bounds in Eiffel are arbitrary, not necessarily 0 or 1, and set on array creation), a.lower and a.upper are the bounds. Here we number the arrays from 1.

There are, as noted, two ways to represent rootness. We must express their consistency (or risk trouble). Two clauses of the invariant do the job:

across roots as t all is_root [t.item] end

across is_root as t all (t.item = roots.has (t.cursor_index)) end

The first one says that if we go through the list roots we only find elements whose is_root value is true; the second, that if we go through the array “is_root” we find values that are true where and only where the corresponding object, given by the cursor index, is in the roots set. Note that the = in that second property is between boolean values (if in doubt, check the type instantly in the EIffelStudio IDE!), so it means “if and only if.

Instead of these clauses, a more concise version, covering them both, is just

roots ~ domain (is_root)

with a function domain that gives the domain of a function represented by a boolean array. The ~ operator denotes object equality, redefined in many classes, and in particular in the SET classes (roots is a LINKED_SET) to cover equality between sets, i.e. the property of having the same elements.

The other clauses are all similarly self-explanatory. Let us just go through the most elaborate one, successors_consistent, involving three levels of across:

across successors as httpl all                   — httpl.item: hash table of list of triples

        across httpl.item as tpl all                — tpl.item: list of triples (tpl.key: key (i.e. tag) in hash table (tag)

                  across tpl.item as tp all            — tp.item: triple

                         tp.item.tag = tpl.key

and tp.item.source = httpl.cursor_index

                   end

          end

end

You can see that I struggled a bit with this one and made provisions for not having to struggle again when I would look at the code again 10 minutes, 10 days or 10 months later. I chose (possibly strange but consistent) names such as httpl for hash-table triple, and wrote comments (I do not usually need any in invariant and other contract clauses) to remind me of the type of everything. That was not strictly needed since once again the IDE gives me the types, but it does not cost much and could help.

What this says: go over successors; which as you remember is an ARRAY, indexed by objects, of HASH_TABLE, where each entry of such a hash table has an element of type [LIST [TRIPLE] and a key of type INTEGER, representing the tag of a number of outgoing edges from the given object. Go over each hash table httpl. Go over the associated list of triples tpl. Then for each triple tp in this list: the tag of the triple must be the key in the hash table entry (remember, the key does denote a tag); and the source of the triple must the object under consideration, which is the current iteration index in the array of the outermost iteration.

I hope I am not scaring you at this point. Although the concepts are simple, this invariant is more sophisticated than most of those we typically write. Many invariant clauses (and preconditions, and postconditions) are very simple properties, such as x > 0 or x ≠ y. The reason this one is more elaborate is not that I am trying to be fussy but that without it I would be the one scared to death. What is elaborate here is the data structure and programming technique. Not rocket science, not anything beyond programmers typically do, but elaborate. The only way to get it right is to buttress it by the appropriate logical properties. As noted, these properties are there anyway, in the back of your head, when you write the program. If you want to be more like an electrical engineer than an electrician, you have to write them down.

There is more to contracts

Invariants are not the only kind of such “contract properties. Here for example, from the same class, is a (slightly abbreviated) part of the postcondition (output property) of the operation that tells us, through a boolean Result, if the multigraph has an edge of given components osource, t (the tag) and otarget :

Result =

(across successors [osource] [t] as tp some

not tp.item.is_inoperative and tp.item.target = otarget

end)

In words, this clause expresses the compatibility of the operation with the successors view: it must answer yes if and only if otarget appears in the successor set of osource for t, and the corresponding triple is not marked inoperative.

The concrete benefits

And so? What do we get out of making these logical properties explicit? Just the intellectual satisfaction of doing things right, and the methodological guidance? No! Once you have done this work, it is all downhill. Turn on the run-time assertion monitoring option (tunable separately for preconditions, postconditions, invariants etc., and on by default in development mode), and watch your tests run. If you are like almost all of us, you will have made a few mistakes, some which will seem silly when or rather if you find them in time (but there is nothing funny about a program that crashes during operation) and some more subtle. Sit back, and just watch your contracts be violated. For example if I change <= to < in the invariant property tw.key <= max_tag, I get the result of Figure 5. I see the call stack that I can traverse, the object run-time structure that I can explore, and all the tools of a modern debugger for an OO language. Finding and correcting the logical flaw will be a breeze.

debugger

Figure 5: An invariant violation brings up the debugger

The difference

It will not be a surprise that I did not get all the data structures and algorithms of the class MULTIGRAPH  right the first time. The Design by Contract approach (the discipline of systematically expressing, whenever you write any software element, the associated logical properties) does lead to fewer mistakes, but everyone occasionally messes up. Everyone also looks at initial results to spot and correct mistakes. So what is the difference?

Without the techniques described here, you execute your software and patiently examine the results. In the example, you might output the content of the data structures, e.g.

List of outgoing references for every object:

        1: 1-1->1|D, 1-1->2|D, 1-1->3|D, 1-2->1|D, 1-2->2|D,  1-25->8|D, 1-7->1|D, 1-7->6|D,

1-10->8|D, 1-3->1|D, 1-3->2|D, 1-6->3|D, 1-6->4|D, 1-6->5|D

        3: 3-6->3, 3-6->4, 3-6->5, 3-9->14, 3-9->15,   3-9->16, 3-1->3, 3-1->2, 3-2->3, 3-2->2,

                  3-25->8, 3-7->3, 3-7->6, 3-10->8, 3-3->3,  3-3->2    

List of outgoing references for every object:

        1: 1-1->1|D, 1-1->2|D, 1-1->3|D, 1-2->1|D, 1-2->2|D, 1-25->8|D, 1-7->1|D, 1-7->6|D,

1-10->8|D, 1-3->1|D,  1-3->2|D, 1-6->3|D, 1-6->4|D, 1-6->5|D

        3: 3-6->3, 3-6->4, 3-6->5, 3-9->14, 3-9->15,  3-9->16, 3-1->3, 3-1->2, 3-2->3, 3-2->2,

                                 3-25->8, 3-7->3, 3-7->6, 3-10->8, 3-3->3,  3-3->2

and so on for all the structures. You check the entries one by one to ascertain that they are as expected. The process nowadays has some automated support, with tools such as JUnit, but it is still essentially manual, tedious and partly haphazard: you write individual test oracles for every relevant case. (For a more automated approach to testing, taking advantage of contracts, see [1].) Like the logical properties appearing in contracts, these oracles are called assertions but the level of abstraction is radically different: an oracle describes the desired result of one test, where a class invariant, or routine precondition, or postcondition expresses the properties desired of all executions.

Compared to the cost of writing up such contract properties (simply a matter of formalizing what you are thinking anyway when you write the code), their effect on testing is spectacular. Particularly when you take advantage of across iterators. In the example, think of all the checks and crosschecks automatically happening across all the data structures, including the nested structures as in the 3-level across clause. Even with a small test suite, you immediately get, almost for free, hundreds or thousands of such consistency checks, each decreasing the likelihood that a logical flaw will survive this ruthless process.

Herein lies the key advantage. Not that you will magically stop making mistakes; but that the result of such mistakes, in the form of contract violations, directly points to logical properties, at the level of your thinking about the program. A wrong entry in an output, whether you detect it visually or through a Junit clause, is a symptom, which may be far from the cause. (Remember Dijkstra’s comment, the real point of his famous Goto paper, about the core difficulty of programming being to bridge the gap between the static program text, which is all that we control, and its effect: the myriad possible dynamic executions.) Since the cause of a bug is always a logical mistake, with a contract violation, which expresses a logical inconsistency, you are much close to that cause.

(About those logical mistakes: since a contract violation reflects a discrepancy between intent, expressed by the contract, and reality, expressed by the code, the mistake may be on either side. And yes, sometimes it is the contract that is wrong while the implementation in fact did what is informally expected. There is partial empirical knowledge [1] of how often this is the case. Even then, however, you have learned something. What good is a piece of code of which you are not able to say correctly what it is trying to do?)

The experience of Eiffel programmers reflects these observations. You catch the mistakes through contract violations; much of the time, you find and correct the problem easily. When you do get to producing actual test output (which everyone still does, of course), often it is correct.

This is what has happened to me so far in the development of the example. I had mistakes, but converging to a correct version was a straightforward process of examining violations of invariant violations and other contract elements, and fixing the underlying logical problem each time.

By the way, I believe I do have a correct version (in the sense of the second part of the Hoare quote), on the basis not of gut feeling or wishful thinking but of solid evidence. As already noted it is hard to imagine, if the code contains any inconsistencies, a test suite surviving all the checks.

Tests and proofs

Solid evidence, not perfect; hard to imagine, not impossible. Tests remain only tests; they cannot exercise all cases. The only way to achieve demonstrable correctness is to rely on mathematical proofs performed mechanically. We have this too, with the AutoProof proof system for Eiffel, developed in recent years [1]. I cannot overstate my enthusiasm for this work (look up the Web-based demo), its results (automated proof of correctness of a full-fledged data structures and algorithms library [2]) and its potential, but it is still a research effort. The dynamic approach (meaning test-based rather than proof-based) presented above is production technology, perfected over several decades and used daily for large-scale mission-critical applications. Indeed (I know you may be wondering) it scales up without difficulty:

  • The approach is progressive. Unlike fully formal methods (and proofs), it does not require you to write down every single property down to the last quantifier. You can start with simple stuff like x > 0. The more you write, the more you get, but it is the opposite of an all-or-nothing approach.
  • On the practical side, if you are wondering about the consequences on performance of a delivered system: there is none. Run-time contract monitoring is a compilation option, tunable for different kinds of contracts (invariants, postconditions etc.) and different parts of a system. People use it, as discussed here, for development, testing and debugging. Most of the time, when you deliver a debugged system, you turn it off.
  • It is easy to teach. As a colleague once mentioned, if you can write an if-then-else you can write a precondition. Our invariants in the above example where a bit more sophisticated, but programmers do write loops (in fact, the Eiffel loop for iterating over a structure also uses across, with loop and instructions instead of all or some and boolean expressions). If you can write a loop over an array, you can write a property of the array’s elements.
  • A big system is an accumulation of small things. In a blog article [5] I recounted how I lost a full day of producing a series of technical diagrams of increasing complexity, using one of the major Web-based collaborative development tools. A bug of the system caused all the diagrams to reproduce the first, trivial one. I managed to get through to the developers. My impression (no more than an educated guess resulting from this interaction) is that the data structures involved were far simpler than the ones used in the above discussion. One can surmise that even simple invariants would have uncovered the bug during testing rather than after deployment.
  • Talking about deployment and tools used directly on the cloud: the action in software engineering today is in DevOps, a rapid develop-deploy loop scheme. This is where my perplexity becomes utter cluelessness. How can anyone even consider venturing into that kind of exciting but unforgiving development model without the fundamental conceptual tools outlined above?

We are back then to the core question. These techniques are simple, demonstrably useful, practical, validated by years of use, explained in professional books (e.g. [6]), introductory programming textbooks (e.g. [7]), EdX MOOCs (e.g. [8]), YouTube videos, online tutorials at eiffel.org, and hundreds of articles cited thousands of times. On the other hand, most people reading this article are not using Eiffel. On reflection, a simple quantitative criterion does exist to identify the inmates: there are far more people outside the asylum than inside. So the evidence is incontrovertible.

What, then, is wrong with me?

References

(Nurse to psychiatrist: these are largely self-references. Add narcissism to list of patient’s symptoms.)

1.    Ilinca Ciupa, Andreas Leitner, Bertrand Meyer, Manuel Oriol, Yu Pei, Yi Wei and others: AutoTest articles and other material on the AutoTest page.

2. Bertrand Meyer, Ilinca Ciupa, Lisa (Ling) Liu, Manuel Oriol, Andreas Leitner and Raluca Borca-Muresan: Systematic evaluation of test failure results, in Workshop on Reliability Analysis of System Failure Data (RAF 2007), Cambridge (UK), 1-2 March 2007 available here.

3.    Nadia Polikarpova, Ilinca Ciupa and Bertrand Meyer: A Comparative Study of Programmer-Written and Automatically Inferred Contracts, in ISSTA 2009: International Symposium on Software Testing and Analysis, Chicago, July 2009, available here.

4.    Carlo Furia, Bertrand Meyer, Nadia Polikarpova, Julian Tschannen and others: AutoProof articles and other material on the AutoProof page. See also interactive web-based online tutorial here.

5.    Bertrand Meyer, The Cloud and Its Risks, blog article, October 2010, available here.

6.    Bertrand Meyer: Object-Oriented Software Construction, 2nd edition, Prentice Hall, 1997.

7.    Bertrand Meyer: Touch of Class: Learning to Program Well Using Objects and Contracts, Springer, 2009, see touch.ethz.ch and Amazon page.

8.    MOOCs (online courses) on EdX : Computer: Art, Magic, Science, Part 1 and Part 2. (Go to archived versions to follow the courses.)

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New paper: making sense of agile methods

Bertrand Meyer: Making Sense of Agile Methods, in IEEE Software, vol. 35, no. 2, March 2018, pages 91-94. IEEE article page here (may require membership or purchase). Draft available here.

An assessment of agile methods, based on my book Agile! The Good, the Hype and the Ugly. It discusses, beyond the hype, the benefits and dangers of agile principles and practices, focusing on concrete examples of what helps and what hurts.

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