It is a common occurrence in software development. Someone says: “We should design a language”. The usual context is that some part of the development requires a rich functionality set, and it appears appropriate to provide a flexible solution through a specialized language. As an example, in the development of an airline’s frequent flyer program on which I once worked the suggestion came to design a “Flyer Award Language” , with instructions appropriate for that application domain: record a trip, redeem an award, provide a statement of available miles and so on. A common term for such notations is DSL, for Domain-Specific Language.
Designing a language in such a context is almost always a bad idea (and I am not sure why I wrote “almost”). Languages are endless objects of discussion, usually on the least important aspects, which are also the most visible and those on which everyone has a strong opinion: concrete syntactic properties. People might pretend otherwise (“let’s not get bogged down on syntax, this is just one possible form”) but syntax is what the discussions will get bogged down to — keywords or symbols, this order or that order of operands, one instruction with several variants vs. several instructions… — at the expense of discussing the fundamental issues of functionality.
Worse yet, even if a language will be part of the solution it is usually just one facet to the solution. As was already explained in detail in [1], any useful functionality set will naturally be useful through several interfaces: a textual notation with concrete syntax may be one of them, but other possible ones include an API (Abstract Program Interface) for use from other software elements, a Graphical User Interface, a web user interface, yet another for web services (typically WSDL or some other XML or JSON format).
In such cases, starting with a concrete textual language is pretty silly, since it cannot yield the others directly (it would have to be parsed and further analyzed, which does not make sense). Of all the kinds of interface listed, the most fundamental one is the API: it describes the raw functionality, excluding any choice of syntax but including, thanks to contracts, elements of semantics. For example, a class AWARD in our frequent flyer application might include the feature
redeem_for_upgrade (c: CUSTOMER; f : FLIGHT)
— Upgrade c to next class of service on f. require c /= holder implies holder.allowed_substitute (c) f.permitted_for_upgrade (Current) c.booked ( f) ensure c.class_of_service ( f ) =old c.class_of_service (f ) + 1
There is of course no implementation as this declaration only specifies an interface, but it says what needs to be said: to redeem the award for an upgrade, the intended customer must be either the holder of the award or an allowed substitute; the flight must be available for an upgrade with the current award (including the availability of enough miles); the intended customer must already be booked on the flight; and the upgrade will be for the next class of service.
These details are the kind of things that need to be discussed and agreed before the API is finalized. Then one can start discussing about a textual form (a DSL), a graphical interface, a web services interface. They all consist of relatively simple layers to be superimposed on a solidly defined and precisely specified basis. Once you have that basis, you can have all the fun you like arguing over everyone’s favorite forms of concrete syntax; it cannot hurt the project any more. Having these discussions early, at the expense of the more fundamental issues, is a great danger.
One of the key rules for successful software construction — as for many other ventures of course, especially in science and technology — is to distinguish the essential from the auxiliary, and consequently to devote proper attention to the essential issues while avoiding disputations of auxiliary issues. To define functionality, API is essential; language is auxiliary.
So when should you design a language? Never. Well, hardly ever.
Reference
[1] Bertrand Meyer: Introduction to the Theory of Programming Languages, Prentice Hall, 1990.
I did not have time to complete my Monday post this week; it will be for next Monday (title: Never design a language). In the meantime, here is the announcement for today’s Saint Petersburg Software Engineering seminar , which can be followed live at http://sel.ifmo.ru/seminar/live (19:30 Saint Petersburg time, meaning 16:30 Zurich/Paris, 7:30 PDT on 12 January 2012), duration about one hour.
I will be talking today; the topic is “The varieties of loop invariants”, reporting on joint work with Carlo Furia and Sergey Velder. The abstract appears below.
A recording of previous talks, starting from those of last week, will soon be available on the seminar page.
Abstract
The key practical issue in verifying software is to come up with the right loop invariants. We are performing an extensive analysis of loop invariants in important algorithms across all major areas of computer science, and have developed a taxonomy. I will present some of the results of this ongoing work, performed with Sergey Velder (ITMO) and Carlo Furia (ETH).
(Acknowledgment: this article came out of a discussion with Manuel Oriol, Carlo Furia and Yi Wei. The material is largely theirs but the opinions are mine.)
A paper on automatic testing, submitted some time ago, received the following referee comment:
The case study seems unrealistic and biased toward the proposed technique. 736 unique faults found in 92 classes means at least 8 unique faults per class at the same time. I have never seen in all my life a published library with so many faults …
This would be a good start for a discussion of what is wrong with refereeing in computer science today (on the negativism of our field see [1]); we have a referee who mistakes experience for expertise, prejudice for truth, and refuses to accept carefully documented evidence because “in all his life”, presumably a rich and rewarding life, he has never seen anything of the sort. That is not the focus of the present article, however; arrogant referees eventually retire and good papers eventually get published. The technical problems are what matters. The technical point here is about testing.
Specifically, what bugs are worth finding, and are high bug rates extraordinary?
The paper under review was a step in the work around the automatic testing tool AutoTest (see [2] for a slightly older overall description and [3] for the precise documentation). AutoTest applies a fully automatic strategy, exercising classes and their routines without the need to provide test cases or test oracles. What makes such automation possible is the combination of random generation of tests and reliance on contracts to determine the success of tests.
For several years we have regularly subjected libraries, in particular the EiffelBase data structure library, to long AutoTest sessions, and we keep finding bugs (the better term is faults). The fault counts are significant; here they caught the referee’s eye. In fact we have had such comments before: I don’t believe your fault counts for production software; your software must be terrible!
Well, maybe.
My guess is that in fact EiffelBase has no more bugs, and possibly far fewer bugs, than other “production” code. The difference is that the AutoTest framework performs far more exhaustive tests than usually practiced.
This is only a conjecture; unlike the referee I do not claim any special powers that make my guesses self-evident. Until we get test harnesses comparable to AutoTest for environments other than Eiffel and, just as importantly, libraries that are fully equipped with contracts, enabling the detection of bugs that otherwise might not come to light, we will not know whether the explanation is the badness of EiffelBase or the goodness of AutoTest.
What concrete, incontrovertible evidence demonstrates is that systematic random testing does find faults that human testers typically do not. In a 2008 paper [4] with Ilinca Ciupa, Manuel Oriol and Alexander Pretschner, we ran AutoTest on some classes and compared the results with those of human testers (as well as actual bug reports from the field, since this was released software). We found that the two categories are complementary: human testers find faults that are still beyond the reach of automated tools, but they typically never find certain faults that AutoTest, with its stubborn dedication to leaving no stone unturned, routinely uncovers. We keep getting surprised at bugs that AutoTest detects and which no one had sought to test before.
A typical set of cases that human programmers seldom test, but which frequently lead to uncovering bugs, involves boundary values. AutoTest, in its “random-plus” strategy, always exercises special values of every type, such as MAXINT, the maximum representable integer. Programmers don’t. They should — all testing textbooks tell them so — but they just don’t, and perhaps they can’t, as the task is often too tedious for a manual process. It is remarkable how many routines using integers go bezerk when you feed them MAXINT or its negative counterpart. Some of the fault counts that seem so outrageous to our referee directly come from trying such values.
Some would say the cases are so extreme as to be insignificant. Wrong. Many documented software failures and catastrophes are due to untested extreme values. Perhaps the saddest is the case of the Patriot anti-missile system, which at the beginning of the first Gulf war was failing to catch Scud missiles, resulting in one case in the killing of twenty-eight American soldiers in an army barrack. It was traced to a software error [5]. To predict the position of the incoming missile, the computation multiplied time by velocity. The time computation used multiples of the time unit, a tenth of a second, stored in a 24-bit register and hence approximated. After enough time, long enough to elapse on the battlefield, but longer than what the tests had exercised, the accumulated error became so large as to cause a significant — and in the event catastrophic — deviation. The unique poser of automatic testing is that unlike human testers it is not encumbered by a priori notions of a situation being extreme or unlikely. It tries all the possibilities it can.
The following example, less portentous in its consequences but just as instructive, is directly related to AutoTest. For his work on model-based contracts [6] performed as part of his PhD completed in 2008 at ETH, Bernd Schoeller developed classes representing the mathematical notion of set. There were two implementations; it turned out that one of them, say SET1, uses data structures that make the subset operation ⊆ easy to program efficiently; in the corresponding class, the superset operation, a ⊇ b, is then simply implemented as b ⊆ a. In the other implementation, say SET2, it is the other way around: ⊇ is directly implemented, and a ⊆ b, is implemented as b ⊇ a. This all uses a nice object-oriented structure, with a general class SET defining the abstract notion and the two implementations inheriting from it.
Now you may see (if you have developed a hunch for automated testing) where this is heading: AutoTest knows about polymorphism and dynamic binding, and tries all the type combinations that make sense. One of the generated test cases has two variables s1 and s2 of type SET, and tries out s2 ⊆ s1; in one of the combinations that AutoTest tries, s1 is dynamically and polymorphically of type SET1 and s2 of type SET2. The version of ⊆ that it will use is from SET2, so it actually calls s1 ⊇ s2; but this tests the SET1 version of ⊇, which goes back to SET2. The process would go on forever, were it not for a timeout in AutoTest that uncovers the fault. Bernd Schoeller had tried AutoTest on these classes not in the particular expectation of finding bugs, but more as a favor to the then incipient development of AutoTest, to see how well the tool could handle model-based contracts. The uncovering of the fault, testament to the power of relentless, systematic automatic testing, surprised us all.
In this case no contract was violated; the problem was infinite recursion, due to a use of O-O techniques that for all its elegance had failed to notice a pitfall. In most cases, AutoTest finds the faults through violated postconditions or class invariants. This is one more reason to be cautious about sweeping generalizations of the kind “I do not believe these bug rates, no serious software that I have seen shows anything of the sort!”. Contracts express semantic properties of the software, which the designer takes care of stating explicitly. In run-of-the-mill code that does not benefit from such care, lots of things can go wrong but remain undetected during testing, only to cause havoc much later during some actual execution.
When you find such a fault, it is irrelevant that the case is extreme, or special, or rare, or trivial. When a failure happens it no longer matter that the fault was supposed to be rare; and you will only know how harmful it is when you deal with the consequences. Testing, single-mindedly devoted to the uncovering of faults [7], knows no such distinction: it hunts all bugs large and small.
References
[1] The nastiness problem in computer science, article on the CACM blog, 22 August 2011, available here.
[2] Bertrand Meyer, Ilinca Ciupa, Andreas Leitner, Arno Fiva, Yi Wei and Emmanuel Stapf: Programs that Test Themselves, IEEE Computer, vol. 42, no. 9, pages 46-55, September 2009, also available here.
[3] Online AutoTest documentation, available here at docs.eiffel.com.
[4] Ilinca Ciupa, Bertrand Meyer, Manuel Oriol and Alexander Pretschner:Finding Faults: Manual Testing vs. Random+ Testing vs. User Reports, in ISSRE ’08, Proceedings of the 19th IEEE International Symposium on Software Reliability Engineering, Redmond, November 2008, available here.
[5] US General Accounting Office: GAO Report: Patriot Missile Defense– Software Problem Led to System Failure at Dhahran, Saudi Arabia, February 4, 1992, available here.
[6] Bernd Schoeller, Tobias Widmer and Bertrand Meyer: Making Specifications Complete Through Models, in Architecting Systems with Trustworthy Components, eds. Ralf Reussner, Judith Stafford and Clemens Szyperski, Lecture Notes in Computer Science, Springer-Verlag, 2006, available here.
[7] Bertrand Meyer: Seven Principles of Software testing, in IEEE Computer, vol. 41, no. 10, pages 99-101, August 2008available here.
I posted here a draft of a new article, Towards a Calculus of Object Programs.
Here is the abstract:
Verifying properties of object-oriented software requires a method for handling references in a simple and intuitive way, closely related to how O-O programmers reason about their programs. The method presented here, a Calculus of Object Programs, combines four components: compositional logic, a framework for describing program semantics and proving program properties; negative variables to address the specifics of O-O programming, in particular qualified calls; the alias calculus, which determines whether reference expressions can ever have the same value; and the calculus of object structures, a specification technique for the structures that arise during the execution of an object-oriented program.
The article illustrates the Calculus by proving the standard algorithm for reversing a linked list.
Lionel Briand and his group at the Simula Research Laboratory in Oslo have helped raise the standard for empirical research in testing and other software engineering practices by criticizing work that in their opinion relies on wrong assumptions or insufficiently supported evidence. In one of their latest papers [1] they take aim at “Adaptive Random Testing” (ART); one of the papers they criticize is from our group at ETH, on the ARTOO extension [2] to this testing method. Let’s examine the criticism!
We need a bit of background on random testing, ART, and ARTOO:
Random testing tries inputs based on a random process rather than attempting a more sophisticated strategy; it was once derided as silly [3], but has emerged in recent years as a useful technique. Our AutoTest tool [4], now integrated in EiffelStudio, has shown it to be particularly effective when applied to code equipped with contracts, which provide built-in test oracles. As a result of this combination, testing can be truly automatic: the two most tedious tasks of traditional testing, test case preparation and test oracle definition, can be performed without human intervention.
ART, developed by Chen and others [5], makes random testing not entirely random by ensuring that the inputs are spread reasonably evenly in the input domain.
ARTOO, part of Ilinca Ciupa’s PhD thesis on testing defended in 2008, generalized ART to object-oriented programs, by defining a notion of distance between objects; the ARTOO strategy avoids choosing objects that are too close to each other. The distance formula, which you can find in[2], combines three elementary distances: between the types of the objects involved, the values in their primitive fields (integers etc.), and, recursively, the objects to which they have references.
Arcuri and Briand dispute the effectiveness of ART and criticize arguments that various papers have used to show its effectiveness. About the ARTOO paper they write
The authors concluded that ART was better than random testing since it needed to sample less test cases before finding the first failure. However, ART was also reported as taking on average 1.6 times longer due to the distance calculations!
To someone not having read our paper the comment and the exclamation mark would seem to suggest that the paper somehow downplays this property of random testing, but in fact it stresses it repeatedly. The property appears for example in boldface as part of the caption to Table 2: In most cases ARTOO requires significantly less tests to find a fault, but entails a time overhead, and again in boldface in the caption to Table 3: The overhead that the distance calculations introduce in the testing process causes ARTOO to require on average 1.6 times more time than RAND to find the first fault.
There is no reason, then, to criticize the paper on this point. It reports the results clearly and fairly.
If we move the focus from the paper to the method, however, Arcuri and Briand have a point. As they correctly indicate, the number of tests to first fault is not a particularly useful criterion. In fact I argued against it in another paper on testing [6]
The number of tests is not that useful to managers, who need help deciding when to stop testing and ship, or to customers, who need an estimate of fault densities. More relevant is the testing time needed to uncover the faults. Otherwise we risk favoring strategies that uncover a failure quickly but only after a lengthy process of devising the test; what counts is total time. This is why, just as flies get out faster than bees, a seemingly dumb strategy such as random testing might be better overall.
(To understand the mention of flies and bees you need to read [6].) The same article states, as its final principle:
Principle 7: Assessment criteria A testing strategy’s most important property is the number of faults it uncovers as a function of time.
The ARTOO paper, which appeared early in our testing work, used “time to first failure” because it has long been a standard criterion in the testing literature, but it should have applied our own advice and focused on more important properties of testing strategies.
The “principles” paper [6] also warned against a risk awaiting anyone looking for new test strategies:
Testing research is vulnerable to a risky thought process: You hit upon an idea that seemingly promises improvements and follow your intuition. Testing is tricky; not all clever ideas prove helpful when submitted to objective evaluation.
The danger is that the clever ideas may result in so much strategy setup time that any benefit on the rest of the testing process is lost. This danger threatens testing researchers, including those who are aware of it.
The idea of ARTOO and object distance remains attractive, but more work is needed to make it an effective contributor to automated random testing and demonstrate that effectiveness. We can be grateful to Arcuri and Briand for their criticism, and I hope they continue to apply their iconoclastic zeal to empirical software engineering work, ours included.
I have objections of my own to their method. They write that “all the work in the literature is based either on simulations or case studies with unreasonably high failure rates”. This is incorrect for our work, which does not use simulations, relying instead on actual, delivered software, where AutoTest routinely finds faults in an automatic manner.
In contrast, however, Arcuri and Briand rely on fault seeding (also known as fault introduction or fault injection):
To obtain more information on how shapes appear in actual SUT, we carried out a large empirical analysis on 11 programs. For each program, a series of mutants were generated to introduce faults in these programs in a systematic way. Faults generated through mutation [allow] us to generate a large number of faults, in an unbiased and varied manner. We generated 3727 mutants and selected the 780 of them with lower detection probabilities to carry out our empirical analysis of faulty region shapes.
In the absence of objective evidence attesting to the realism of fault seeding, I do not believe any insights into testing obtained from such a methodology. In fact we adopted, from the start of our testing work, the principle that we would never rely on fault seeding. The problem with seeded faults is that there is no guarantee they reflect the true faults that programmers make, especially the significant ones. Techniques for fault seeding are understandably good at introducing typographical mistakes, such as a misspelling or the replacement of a “+” by a “-”; but these are not interesting kinds of fault, as they are easily caught by the compiler, by inspection, by low-tech static tools, or by simple tests. Interesting faults are those resulting from a logical error in the programmer’s mind, and in my experience (I do not know of good empirical studies on this topic) seeding techniques do not generate them.
For these reasons, all our testing research has worked on real software, and all the faults that AutoTest has found were real faults, resulting from a programmer’s mistake.
We can only apply this principle because we work with software equipped with contracts, where faults will be detected through the automatic oracle of a violated assertion clause. It is essential, however, to the credibility and practicality of any testing strategy; until I see evidence to the contrary, I will continue to disbelieve any testing insights resulting from studies based on artificial fault injection.
References
[1] Andrea Arcuri and Lionel Briand: Adaptive Random Testing: An Illusion of Effectiveness, in ISSTA 2011 (International Symposium on Software Testing and Analysis), available here.
[2] Ilinca Ciupa, Andreas Leitner, Manuel Oriol and Bertrand Meyer: ARTOO: Adaptive Random Testing for Object-Oriented Software, in ICSE 2008: Proceedings of 30th International Conference on Software Engineering, Leipzig, 10-18 May 2008, IEEE Computer Society Press, 2008, also available here.
[3] Glenford J. Myers. The Art of Software Testing. Wiley, New York, 1979. Citation:
Probably the poorest methodology of all is random-input testing: the process of testing a program by selecting, at random, some subset of all possible input values. In terms of the probability of detecting the most errors, a randomly selected collection of test cases has little chance of being an optimal, or close to optimal, subset. What we look for is a set of thought processes that allow one to select a set of test data more intelligently. Exhaustive black-box and white-box testing are, in general, impossible, but a reasonable testing strategy might use elements of both. One can develop a reasonably rigorous test by using certain black-box-oriented test-case-design methodologies and then supplementing these test cases by examining the logic of the program (i.e., using white-box methods).
[4] Bertrand Meyer, Ilinca Ciupa, Andreas Leitner, Arno Fiva, Yi Wei and Emmanuel Stapf: Programs that Test Themselves, IEEE Computer, vol. 42, no. 9, pages 46-55, September 2009, available here. For practical uses of AutoTest within EiffelStudio see here.
[5] T. Y. Chen, H Leung and I K Mak: Adaptive Random Testing, in Advances in Computer Science, ASIAN 2004, Higher-Level Decision Making, ed. M.J. Maher, Lecture Notes in Computer Science 3321, Springer-Verlag, pages 320-329, 2004, available here.
[6] Bertrand Meyer: Seven Principles of Software testing, in IEEE Computer, vol. 41, no. 10, pages 99-101, August 2008, also available here.
We often need to be reassured that a routine, usually a function, is “pure”, meaning that it does not change the state of the computation. For example, a function used in a contract element (precondition, postcondition, class invariant, loop invariant) should be purely descriptive, since it is a specification element; evaluating it, typically for testing and debugging, should not create a change of behavior — a “Heisenberg effect” — in the very computation that it is intended to assess. Another application is in a concurrency context, particularly in SCOOP (see earlier posts and forthcoming ones): if one or more functions are pure, several of their executions can run concurrently on the same object.
The notion of purity admits variants. The usual notion is what [2] calls weak purity, which precludes changes to previously existing objects but allow creating new objects. In the EiffelBase library we also encounter routines that have another form of purity, which we may call “relative” purity: they can leave the same state on exit as they found on entry, but in-between they might change the state. For the rest of this discussion we will rely on the standard notion of weak purity: no changes permitted on existing objects.
It is often suggested that the programming language should support specifying that a routine is pure; many people have indeed proposed the addition of a keyword such as pureto Eiffel. One of the reasons this is not — in my opinion — such a great idea is that purity is just a special case of the more general problem of framing: specifying and verifying what a routine does not change. If we can specify an arbitrary frame property, then we can, as a special case covered by the general mechanism, specify that a routine changes nothing.
To see why framing is so important, consider a class ACCOUNT with a routine deposit that has the postcondition
balance = old balance + sum………..— Where sum is the argument of deposit
Framing here means stating that nothing else than balance changes; for example the account’s owner and its number should remain the same. It is not practical to write all individual postcondition clauses such as
owner= old owner number= old number
and so on. But we do need to specify these properties and enforce them, if only to avoid that a descendant class (maybe MAFIA_ACCOUNT) distort the rules defined by the original.
One technique is to add a so-called “modifies clause”, introduced by verification tools such as ESC-Java [3] and JML [4]. Modifies clauses raise some theoretical issues; in particular, the list of modified expressions is often infinite, so we must restrict ourselves to an abstract-data-type view where we characterize a class by commands and queries and the modifies clause only involves queries of the class. Many people find this hard to accept at first, since anything that is not talked about can change, but it is the right approach. A modifies clause of sorts, included in the postcondition, appeared in an earlier iteration of the Eiffel specification, with the keyword only (which is indeed preferable to modifies, which in the Eiffel style favoring grammatically simple keywords would be modify, since what we want to express is not that the routine must change anything at all but that it may only change certain specified results!). The convention worked well with inheritance, since it included the rule that a clause such as only balance, in class ACCOUNT, prescribes that the routine may not, in its modifies version as well as in any version redefined in descendants, change any other query known at the level of ACCOUNT; but a descendant version may change, subject to its own ACCOUNTclauses, any new query introduced by a descendant.
To declare a routine as pure, it would suffice to use an empty only clause (not very elegant syntactically — “only” what? — but one can get used to it).
This construct has been discarded, as it places too heavy a burden on the programmer-specifier. Here the key observation resulted from a non-scientific but pretty extensive survey I made of all the JML code I could get my hands on. I found that every time a query appeared in a modifies clause it was also listed in the postcondition! On reflection, this seems reasonable: if you are serious about specification, as anyone bothering to write such a clause surely is, you will not just express that something changes and stop there; you will write something about how it may change. Not necessarily the exact result, as in
my_query = precise_final_value
but at least some property of that result, as in
some_property (my_query)
This observation has, however, an inescapable consequence for language design: modifies or only clauses should be inferred by the compiler from the postcondition, not imposed on the programmer as an extra burden. The convention, which we may call the Implicit Framing Rule, is simple:
A routine may change the value of a query only if the query is specified in the routine’s postcondition
(or, if you like double negation, “no routine may change the value of a query other than those specified in its postcondition”). Here we say that a feature is “specified” in a postcondition if it appears there outside of the scope of an old expression. (Clearly, an occurrence such as old balance does not imply that balancecan be modified, hence this restriction to occurrences outside of an old.)
With this convention the only clause becomes implicit: it would simply list all the queries specified in the postcondition, so there is no need for the programmer to write it. For the rare case of wanting to specify that a query q may change, but not wanting to specify how, it is easy to provide a library function, say involved, that always return true and can be used in postconditions, as in involved (q).
The convention is of course not just a matter of programming methodology but, in an IDE supporting verification, such as the EVE “Verification As a Matter Of Course” environment which we are building for Eiffel [5], the compiler will enforce the definition above — no change permitted to anything not specified in the postcondition — and produce an error in case of a violation. This also means that we can easily specify that a routine is pure: it must simply not specify any query in its postcondition. It may still list it in an old clause, as happens often in practice, e.g.
Result = old balance – Minimum_balance………..— In the postcondition of a function available_funds
Note the need to use old here. Apart from this addition of old to some postconditions, a considerable advantage of the convention is that most existing code using pure functions will be suitable to the new purity enforcement without any need to provide new annotations.
I believe that this is the only sustainable convention. It does not, of course, solve the frame problem by itself (for attempts in this direction see [6, 7]), but it is a necessary condition for a solution that is simple, easily taught, fairly easily implemented, and effective. It goes well with model-based specifications [8, 9], which I believe are the technique of most promise for usable specifications of object-oriented software. And it provides a straightforward, no-frills way to enforce purity where prescribed by the Command-Query Separation principle [10, 11]: if I’m not pure, at least my functions must be.
References
[1] From the lyrics of the aria Glitter and Be Gay in Leonard Bernstein’s Candide, text by Lillian Hellman and others. Youtube offers several performances, including by Diana Damrau (here) and Natalie Dessay (here) . For the text see e.g. here.
[2] Adam Darvas and Peter Müller: Reasoning About Method Calls in Interface Specifications, in Journal of Object Technology, Volume 5, no. 5, jUNE 2006, pages 59-85, doi:10.5381/jot.2006.5.5.a3, available here.
[3] C. Flanagan, K.R.M. Leino, M. Lillibridge, G. Nelson, J. B. Saxe and R. Stata: Extended static checking for Java, in PLDI 2002 (Programming Language Design and Implementation), pages 234–245, 2002.
[4] Gary Leavens et al.: Java Modeling Language, see references here.
[5] Julian Tschannen, Carlo A. Furia, Martin Nordio, and Bertrand Meyer: Verifying Eiffel Programs with Boogie, to appear in Boogie 2011, First International Workshop on Intermediate Verification Languages, Wroclaw, August 2011. See documentation about the EVE project on the project page.
[6] Ioannis Kassios: Dynamic Frames: Support for Framing, Dependencies and Sharing Without Restrictions, in Formal Methods 2006, eds. J. Misra, T. Nipkow and E. Sekerinski, Lecture Notes in Computer Science 4085, Springer Verlag, 2006, pages 268-283.
[7] Bernd Schoeller: Making Classes Provable through Contracts, Models and Frames, PhD thesis, ETH Zurich, 2007, available here
[8] Bernd Schoeller, Tobias Widmer and Bertrand Meyer: Making Specifications Complete Through Models, in Architecting Systems with Trustworthy Components, eds. Ralf Reussner, Judith Stafford and Clemens Szyperski, Lecture Notes in Computer Science, Springer-Verlag, 2006, available here.
[9] Nadia Polikarpova, Carlo Furia and Bertrand Meyer: Specifying Reusable Components, in Verified Software: Theories, Tools, Experiments (VSTTE ’10), Edinburgh, UK, 16-19 August 2010, Lecture Notes in Computer Science, Springer Verlag, 2010, available here.
[10] Bertrand Meyer: Object-Oriented Software Construction, first (1988) and second (1997) editions, Prentice Hall.
[11] Bertrand Meyer: Touch of Class: An Introduction to Programming Well, Using Objects and Contracts, Springer Verlag, 2009.
The software world is not flat; it is multipolar. Gone are the days of one-site, one-team developments. The increasingly dominant model today is a distributed team; the place where the job gets done is the place where the appropriate people reside, even if it means that different parts of the job get done in different places.
This new setup, possibly the most important change to have affected the practice of software engineering in this early part of the millennium, has received little attention in the literature; and even less in teaching techniques. I got interested in the topic several years ago, initially by looking at the phenomenon of outsourcing from a software engineering perspective [1]. At ETH, since 2004, Peter Kolb and I, aided by Martin Nordio and Roman Mitin, have taught a course on the topic [2], initially called “software engineering for outsourcing”. As far as I know it was the first course of its kind anywhere; not the first course about outsourcing, but the first to explore the software engineering implications, rather than business or political issues. We also teach an industry course on the same issues [3], attended since 2005 by several hundred participants, and started, with Mathai Joseph from Tata Consulting Services, the SEAFOOD conference [4], Software Engineering Advances For Outsourced and Offshore Development, whose fourth edition starts tomorrow in Saint Petersburg.
After a few sessions of the ETH course we realized that the most important property of the mode of software development explored in the course is not that it involves outsourcing but that it is distributed. In parallel I became directly involved with highly distributed development in the practice of Eiffel Software’s development. In 2007 we renamed the ETH course “Distributed and Outsourced Software Engineering” (DOSE) to acknowledge the broadened scope. The topic is still new; each year we learn a little more about what to teach and how to teach it.
The 2007 session saw another important addition. We felt it was no longer sufficient to talk about distributed development, but that students should practice it. Collaboration between groups in Zurich and other groups in Zurich was not good enough. So we contacted colleagues around the world interested in similar issues, and received an enthusiastic response. The DOSE project is itself distributed: teams from students in different universities collaborate in a single development. Typically, we have two or three geographically distributed locations in each project group. The participating universities have been Politecnico di Milano (where our colleagues Carlo Ghezzi and Elisabetta di Nitto have played a major role in the current version of the project), University of Nijny-Novgorod in Russia, University of Debrecen in Hungary, Hanoi University of Technology in Vietnam, Odessa National Polytechnic in the Ukraine and (across town for us) University of Zurich. For the first time in 2010 a university from the Western hemisphere will join: University of Rio Cuarto in Argentina.
We have extensively studied how the projects actually fare (see publications [4-8]). For students, the job is hard. Often, after a couple of weeks, many want to give up: they have trouble reaching their partner teams, understanding their accents on Skype calls, agreeing on modes of collaboration, finalizing APIs, devising a proper test plan. Yet they hang on and, in most cases, succeed. At the end of the course they tell us how much they have learned about software engineering. For example I know few better way of teaching the importance of carefully documented program interfaces — including contracts — than to ask the students to integrate their modules with code from another team halfway around the globe. This is exactly what happens in industrial software development, when you can no longer rely on informal contacts at the coffee machine or in the parking lot to smooth out misunderstandings: software engineering principles and techniques come in full swing. With DOSE, students learn and practice these fundamental techniques in the controlled environment of a university project.
An example project topic, used last year, was based on an idea by Martin Nordio. He pointed out that in most countries there are some card games played in that country only. The project was to program such a game, where the team in charge of the game logic (what would be the “business model” in an industrial project) had to explain enough of their country’s game, and abstractly enough, to enable the other team to produce the user interface, based on a common game engine started by Martin. It was tough, but some of the results were spectacular, and these are students who will not need more preaching on the importance of specifications.
We are currently preparing the next session of DOSE, in collaboration with our partner universities. The more the merrier: we’d love to have other universities participate, including from the US. Adding extra spice to the project, the topic will be chosen among those from the ICSE SCORE competition [9], so that winning students have the opportunity to attend ICSE in Hawaii. If you are teaching a suitable course, or can organize a student group that will fit, please read the project description [10] and contact me or one of the other organizers listed on the page. There is a DOSE of madness in the idea, but no one, teacher or student, ever leaves the course bored.
References
[1] Bertrand Meyer: Offshore Development: The Unspoken Revolution in Software Engineering, in Computer (IEEE), January 2006, pages 124, 122-123. Available here.
[5] 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. Preprint version available online.
[6] Bertrand Meyer: Design and Code Reviews in the Age of the Internet, in Communications of the ACM, vol. 51, no. 9, September 2008, pages 66-71. (Original version in Proceedings of SEAFOOD 2008 (Software Engineering Advances For Offshore and Outsourced Development, Lecture Notes in Business Information Processing 16, Springer Verlag, 2009.) Available online.
[7] Martin Nordio, Roman Mitin, Bertrand Meyer, Carlo Ghezzi, Elisabetta Di Nitto and Giordano Tamburelli: The Role of Contracts in Distributed Development, in Proceedings of SEAFOOD 2009 (Software Engineering Advances For Offshore and Outsourced Development), Zurich, June-July 2009, Lecture Notes in Business Information Processing 35, Springer Verlag, 2009. Available online.
[8] Martin Nordio, Roman Mitin and Bertrand Meyer: Advanced Hands-on Training for Distributed and Outsourced Software Engineering, in ICSE 2010: Proceedings of 32th International Conference on Software Engineering, Cape Town, May 2010, IEEE Computer Society Press, 2010. Available online.
I am blogging live from the “Cloud Futures” conference organized by Microsoft in Redmond [1]. We had two excellent keynotes today, by Ed Lazowska [1] and David Patterson.
Lazowska emphasized the emergence of a new kind of science — eScience — based on analysis of enormous amounts of data. His key point was that this approach is a radical departure from “computational science” as we know it, based mostly on large simulations. With the eScience paradigm, the challenge is to handle the zillions of bytes of data that are available, often through continuous streams, in such fields as astronomy, oceanography or biology. It is unthinkable in his view to process such data through super-computing architectures specific to an institution; the Cloud is the only solution. One of the reasons (developed more explicitly in Patterson’s talk) is that cloud computing supports scaling down as well as scaling up. If your site experiences sudden bursts of popularity — say you get slashdotted — followed by downturns, you just cannot size the hardware right.
Lazowska also noted that it is impossible to convince your average university president that Cloud is the way to go, as he will get his advice from the science-by-simulation types. I don’t know who the president is at U. of Washington, but I wonder if the comment would apply to Stanford?
The overall argument for cloud computing is compelling. Of course the history of IT is a succession of swings of the pendulum between centralization and delocalization: mainframes, minis, PCs, client-server, “thin clients”, “The Network Is The Computer” (Sun’s slogan in the late eighties), smart clients, Web services and so on. But this latest swing seems destined to define much of the direction of computing for a while.
Interestingly, no speaker so far has addressed issues of how to program reliably for the cloud, even though cloud computing seems only to add orders of magnitude to the classical opportunities for messing up. Eiffel and contracts have a major role to play here.
More generally the opportunity to improve quality should not be lost. There is a widespread feeling (I don’t know of any systematic studies) that a non-negligible share of results generated by computational science are just bogus, the product of old Fortran programs built by generations of graduate students with little understanding of software principles. At the very least, moving to cloud computing should encourage the use of 21-th century tools, languages and methods. Availability on the cloud should also enhance a critical property of good scientific research: reproducibility.
Software engineering is remarkably absent from the list of scientific application areas that speaker after speaker listed for cloud computing. Maybe software engineering researchers are timid, and do not think of themselves as deserving large computing resources; consider, however, all the potential applications, for example in program verification and empirical software engineering. The cloud is a big part of our own research in verification; in particular the automated testing paradigm pioneered by AutoTest [3] fits ideally with the cloud and we are actively working in this direction.
Lazowska mentioned that development environments are the ultimate application of cloud computing. Martin Nordio at ETH has developed, with the help of Le Minh Duc, a Master’s student at Hanoi University of Technology, a cloud-based version of EiffelStudio: CloudStudio, which I will present in my talk at the conference tomorrow. I’ll write more about it in later posts; just one note for the moment: no one should ever be forced again to update or commit.
[3] Bertrand Meyer, Arno Fiva, Ilinca Ciupa, Andreas Leitner, Yi Wei, Emmanuel Stapf: Programs That Test Themselves. IEEE Computer, vol. 42, no. 9, pages 46-55, September 2009; online version here.
Let me start, dear reader of this blog, by probing your view of equality, and also of assignment. Two questions:
Is a value x always equal to itself? (For the seasoned programmers in the audience: I am talking about a value, as in mathematics, not an expression, as in programming, which could have a side effect.)
In programming, if we consider an assignment
x := y
and v is the value of y before that assignment (again, this little detour is to avoid bothering with side effects), is the value of x always equal to v after the assignment?
Maybe I should include here one of these Web polls that one finds on newspaper sites, so that you can vote and compare your answer to the Wisdom of Crowds. My own vote is clear: yes to both. Equality is reflexive (every value is equal to itself, at any longitude and temperature, no excuses and no exceptions); and the purpose of assignment is to make the value of the target equal to the value of the source. Such properties are some of the last ramparts of civilization. If they go away, what else is left?
754 enters the picture
Now come floating-point numbers and the famous IEEE “754” floating-point standard [1]. Because not all floating point operations yield a result usable as a floating-point number, the standard introduces a notion of “NaN”, Not a Number; certain operations involving floating-point numbers may yield a NaN. The term NaN does not denote a single value but a set of values, distinguished by their “payloads”.
Now assume that the value of x is a NaN. If you use a programming language that supports IEEE 754 (as they all do, I think, today) the test in
ifx = xthen …
is supposed to yield False. Yes, that is specified in the standard: NaN is never equal to NaN (even with the same payload); nor is it equal to anything else; the result of an equality comparison involving NaN will always be False.
Assignment behavior is consistent with this: if y (a variable, or an expression with no side effect) has a NaN value, then after
x := y
the test x = y will yield False.
Before commenting further let me note the obvious: I am by no means a numerics expert; I know that IEEE 754 was a tremendous advance, and that it was designed by some of the best minds in the field, headed by Velvel Kahan who received a Turing Award in part for that success. So it is quite possible that I am about to bury myself in piles of ridicule. On the other hand I have also learned that (1) ridicule does not kill, so I am game; and more importantly (2) experts are often right but not always, and it is always proper to question their reasoning. So without fear let me not stop at the arguments that “the committee must have voted on this point and they obviously knew what they were doing” and “it is the standard and implemented on zillions of machines, you cannot change it now”. Both are true enough, but not an excuse to censor questions.
What are the criteria?
The question is: compatibility with an existing computer standard is great, but what about compatibility with a few hundred years of mathematics? Reflexivity of equality is something that we expect for any data type, and it seems hard to justify that a value is not equal to itself. As to assignment, what good can it be if it does not make the target equal to the source value?
The question of assignment is particularly vivid in Eiffel because we express the expected abstract properties of programs in the form of contracts. For example, the following “setter” procedure may have a postcondition (expressed by the ensure clause):
set_x (v: REAL)
— Set the value of x (an attribute, also of type REAL) the value of v. do
… x := v ensure x = v end
If you call this procedure with a NaN argument for a compiler that applies IEEE 754 semantics, and monitor contracts at run time for testing and debugging, the execution will report a contract violation. This is very difficult for a programmer to accept.
A typical example arises when you have an assignment to an item of an array of REAL values. Assume you are executing a [i] := x. In an object-oriented view of the world (as in Eiffel), this is considered simplified syntax for the routine call a.put (x, i). The postcondition is that a [i] = x. It will be violated!
The experts’ view
I queried a number of experts on the topic. (This is the opportunity to express my gratitude to members of the IFIP working group 2.5 on numerical software [2], some of the world’s top experts in the field, for their willingness to respond quickly and with many insights.) A representative answer, from Stuart Feldman, was:
If I remember the debate correctly (many moons ago), NaN represents an indefinite value, so there is no reason to believe that the result of one calculation with unclear value should match that of another calculation with unclear value. (Different orders of infinity, different asymptotic approaches toward 0/0, etc.)
Absolutely correct! Only one little detail, though: this is an argument against using the value True as a result of the test; but it is not an argument for using the value False! The exact same argument can be used to assert that the result should not be False:
… there is no reason to believe that the result of one calculation with unclear value should notmatch that of another calculation with unclear value.
Just as convincing! Both arguments complement each other: there is no compelling reason for demanding that the values be equal; and there is no compelling argument either to demand that they be different. If you ignore one of the two sides, you are biased.
What do we do then?
The conclusion is not that the result should be False. The rational conclusion is that True and False are both unsatisfactory solutions. The reason is very simple: in a proper theory (I will sketch it below) the result of such a comparison should be some special undefined below; the same way that IEEE 754 extends the set of floating-point numbers with NaN, a satisfactory solution would extend the set of booleans with some NaB (Not a Boolean). But there is no NaB, probably because no one (understandably) wanted to bother, and also because being able to represent a value of type BOOLEAN on a single bit is, if not itself a pillar of civilization, one of the secrets of a happy life.
If both True and False are unsatisfactory solutions, we should use the one that is the “least” bad, according to some convincing criterion . That is not the attitude that 754 takes; it seems to consider (as illustrated by the justification cited above) that False is somehow less committing than True. But it is not! Stating that something is false is just as much of a commitment as stating that it is true. False is no closer to NaB than True is. A better criterion is: which of the two possibilities is going to be least damaging to time-honored assumptions embedded in mathematics? One of these assumptions is the reflexivity of equality: come rain or shine, x is equal to itself. Its counterpart for programming is that after an assignment the target will be equal to the original value of the source. This applies to numbers, and it applies to a NaN as well.
Note that this argument does not address equality between different NaNs. The standard as it is states that a specific NaN, with a specific payload, is not equal to itself.
What do you think?
A few of us who had to examine the issue recently think that — whatever the standard says at the machine level — a programming language should support the venerable properties that equality is reflexive and that assignment yields equality.
Every programming language should decide this on its own; for Eiffel we think this should be the specification. Do you agree?
Some theory
For readers who like theory, here is a mathematical restatement of the observations above. There is nothing fundamentally new in this section, so if you do not like strange symbols you can stop here.
The math helps explain the earlier observation that neither True nor False is more“committing” than the other. A standard technique (coming from denotational semantics) for dealing with undefinedness in basic data types, is to extend every data type into a lattice, with a partial order relation meaning “less defined than”. The lattice includes a bottom element, traditionally written “⊥” (pronounced “Bottom”) and a top element written ⊤ (“Top”). ⊥ represents an unknown value (not enough information) and ⊤ an error value (too much information). Pictorially, the lattice for natural numbers would look like this:
The lattice of integers
On basic types, we always use very simple lattices of this form, with three kinds of element: ⊥, less than every other element; ⊤, larger than all other elements; and in-between all the normal values of the type, which for the partial order of interest are all equal. (No, this is not a new math in which all integers are equal. The order in question simply means “is less defined than”. Every integer is as defined as all other integers, more defined than ⊥, and less defined than ⊤.) Such lattices are not very exciting, but they serve as a starting point; lattices with more interesting structures are those applying to functions on such spaces — including functions of functions —, which represent programs.
The modeling of floating-point numbers with NaN involves such a lattice; introducing NaN means introducing a ⊥ value. (Actually, one might prefer to interpret NaN as ⊤, but the reasoning transposes immediately.) More accurately, since there are many NaN values, the lattice will look more like this:
The lattice of floats in IEEE 754
For simplicity we can ignore the variety of NaNs and consider a single ⊤.
Functions on lattices — as implemented by programs — should satisfy a fundamental property: monotonicity. A function f is monotone (as in ordinary analysis) if, whenever x ≤ y, then f (x) ≤ f (y). Monotonicity is a common-sense requirement: we cannot get more information from less information. If we know less about x than about y, we cannot expect that any function (with a computer, any program) f will, out of nowhere, restore the missing information.
Demanding monotonicity of all floating-point operations reflects this exigency of monotonicity: indeed, in IEEE 754, any arithmetic operation — addition, multiplication … — that has a NaN as one of its arguments must yield a Nan as its result. Great. Now for soundness we should also have such a property for boolean-valued operations, such as equality. If we had a NaB as the ⊥ of booleans, just like NaN is the ⊥ of floating-point numbers, then the result of NaN = NaN would be NaB. But the world is not perfect and the IEEE 754 standard does not govern booleans. Somehow (I think) the designers thought of False as somehow less defined than True. But it is not! False is just as defined as True in the very simple lattice of booleans; according to the partial order, True and False are equal for the relevant partial order:
The lattice of booleans
Because any solution that cannot use a NaB will violate monotonicity and hence will be imperfect, we must resort to heuristic criteria. A very strong criterion in favor of choosing True is reflexivity — remaining compatible with a fundamental property of equality. I do not know of any argument for choosing False.
The Spartan approach
There is, by the way, a technique that accepts booleans as we love them (with two values and no NaB) and achieves mathematical rigor. If operations involving NaNs truly give us pimples, we can make any such operation trigger an exception. In the absence of ⊥ values, this is an acceptable programming technique for representing undefinedness. The downside, of course, is that just about everywhere the program must be ready to handle the exception in some way.
It is unlikely that in practice many people would be comfortable with such a solution.
Final observations
Let me point out two objections that I have seen. Van Snyder wrote:
NaN is not part of the set of floating-point numbers, so it doesn’t behave as if “bottom” were added to the set.
Interesting point, but, in my opinion not applicable: ⊥ is indeed not part of the mathematical set of floating point numbers, but in the form of NaN it is part of the corresponding type (float in C, REAL in Eiffel); and the operations of the type are applicable to all values, including NaN if, as noted, we have not taken the extreme step of triggering an exception every time an operation uses a NaN as one of its operands. So we cannot free ourselves from the monotonicity concern by just a sleight of hands.
Another comment, also by Van Snyder (slightly abridged):
Think of [the status of NaN] as a variety of dynamic run-time typing. With static typing, if x is an integer variable and y
x := y
does not inevitably lead to
x = y
True; and a compelling argument to require that conversions satisfy equality as a postcondition! Such reasoning — reflexivity again — was essential in the design of the Eiffel conversion mechanism [3], which makes it possible to define conversions between various data types (not just integers and reals and the other classical examples, but also any other user types as long as the conversion does not conflict with inheritance). The same conversion rules apply to assignment and equality, so that yes, whenever the assignment x := y is permitted, including when it involves a conversion, the property x = y is afterwards always guaranteed to hold.
It is rather dangerous indeed to depart from the fundamental laws of mathematics.
References
[1] IEEE floating-point standard, 754-2008; see summary and references in the Wikipedia entry.
[2] IFIP Working Group 2.5 on numerical software: home page.
New variants of the loop construct have been introduced into Eiffel, allowing a safer, more concise and more abstract style of programming. The challenge was to remain compatible with the basic loop concept, in particular correctness concerns (loop invariant and loop variant), to provide a flexible mechanism that would cover many different cases of iteration, and to keep things simple.
Here are some examples of the new forms. Consider a structure s, such as a list, which can be traversed in sequence. The following loop prints all elements of the list:
acrosssasc loopprint (c.item) end
(The procedure print is available on all types, and can be adapted to any of them by redefining the out feature; item gives access to individual values of the list.) More about c in just a moment; in the meantime you can just consider consider that using “asc” and manipulating the structure through c rather than directly through s is a simple idiom to be learned and applied systematically for such across loops.
The above example is an instruction. The all and some variants yield boolean expressions, as in
across sascallc.item > 0end
which denotes a boolean value, true if and only if all elements of the list are positive. To find out if at least one is positive, use
across sascsome c.item > 0end
Such expressions could appear, for example, in a class invariant, but they may be useful in many different contexts.
In some cases, a from clause is useful, as in
across sascfromsum := 0 loop sum := sum + c.index ∗ c.itemend
— Computes Σ i * s [i]
The original form of loop in Eiffel is more explicit, and remains available; you can achieve the equivalent of the last example, on a suitable structure, as
A list and a cursor
from sum := 0 ; s.start until s.after loop sum := sum + s.index ∗ s.item
s.forth end
which directly manipulates a cursor through s, using start to move it to the beginning, forth to advance it, and after to test if it is past the last element. The forms with across achieve the same purpose in a more concise manner. More important than concision is abstraction: you do not need to worry about manipulating the cursor through start, forth and after. Under the hood, however, the effect is the same. More precisely, it is the same as in a loop of the form
from sum := 0 ; c.start until c.after loop sum := sum + c.index ∗ c.item
c.forth end
wherec is a cursor object associated with the loop. The advantage of using a cursor is clear: rather than keeping the state of the iteration in the object itself, you make it external, part of a cursor object that, so to speak, looks at the list. This means in particular that many traversals can be active on the same structure at the same time; with an internal cursor, they would mess up with each other, unless you manually took the trouble to save and restore cursor positions. With an external cursor, each traversal has its own cursor object, and so does not interfere with other traversals — at least as long as they don’t change the structure (I’ll come back to that point).
With the across variant, you automatically use a cursor; you do not have to declare or create it: it simply comes as a result of the “asc” part of the construct, which introduces c as the cursor.
On what structures can you perform such iterations? There is no limitation; you simply need a type based on a class that inherits, directly or indirectly, from the library class ITERABLE. All relevant classes from the EiffelBase library have been updated to provide this inheritance, so that you can apply the across scheme to lists of all kinds, hash tables, arrays, intervals etc.
One of these structures is the integer interval. The notation m |..| n, for integers m and n, denotes the corresponding integer interval. (This is not a special language notation, simply an operator, |..|, defined with the general operator mechanism as an alias for the feature interval of INTEGER classes.) To iterate on such an interval, use the same form as in the examples above:
acrossm |..| nascfromsum := 0 loop sum := sum + a [c.item] end
— Computes Σ a [i], for i ranging from m to n, for an array (or other structure) a.
The key feature in ITERABLE is new_cursor, which returns a freshly created cursor object associated with the current structure. By default it is an ITERATION_CURSOR, the most general cursor type, but every descendant of ITERABLE can redefine the result type to something more specific to the current structure. Using a cursor — c in the above examples —, rather than manipulating the structure s directly, provides considerable flexibility thanks to the property that ITERATION_CURSOR itself inherits from ITERABLE and hence has all the iteration mechanisms. For example you may write
across s.new_cursor.reversedascloop print (c.item) end
to print elements in reverse order. (Using Eiffel’s operator mechanism, you may write –s.new_cursor, with a minus operator, as a synonym fornew_cursor.reversed.) The function reversed gives you a new cursor on the same target structure, enabling you to iterate backwards. Or you can use
across s.new_cursor + 3 ascloop print (c.item) end
(using s.new_cursor.incremented (3) rather than s.new_cursor + 3 if you are not too keen on operator syntax) to iterate over every third item. A number of other possibilities are available in the cursor classes.
A high-level iteration mechanism is only safe if you have the guarantee that the structure remains intact throughout the iteration. Assume you are iterating through a structure
acrosss ascloop some_routineend
and some_routine changes the structure s: the whole process could be messed up. Thanks to Design by Contract mechanisms, the library protects you against such mistakes. Features such as item and index, accessing properties of the structure during the iteration, are equipped with a precondition clause
require is_valid
and every operation that changes the structure sets is_valid to false. So as soon as any change happens, you cannot continue the iteration; all you can do is restart a new one; the command start, used internally to start the operation, does not have the above precondition.
Sometimes, of course, you do want to change a structure while traversing it; for example you may want to add an element just to the right of the iteration position. If you know what you are doing that’s fine. (Let me correct this: if you know what you are doing, express it through precise contracts, and you’ll be fine.) But then you should not use the abstract forms of the loop, across…; you should control the iteration itself through the basic form from … until … with explicit cursor manipulation, protected by appropriate contracts.
The two styles, by the way, are not distinct constructs. Eiffel has always had only one form of loop and this continues the case. The across forms are simply new possibilities added to the classical loop construct, with obvious constraints stating for example that you may not have both a some or all form and an explicit loop body. In particular, an across loop can still have an invariant clause , specifying the correctness properties of the loop, as in
across sascfromsum := 0 invariant sum = sigma (s, index) loop sum := sum + c.index ∗ c.itemend
EiffelStudio 6.5 already included the language update; the library update (not yet including the is_valid preconditions for data structure classes) will be made available this week.
These new mechanisms should increase the level of abstraction and the reliability of loops, a fundamental element of almost all programs.
