Archive for the ‘Software design’ Category.

A remarkable group photo

On 13-15 September 1999 a symposium took place in St Catherine College in Oxford,  in honor of Tony Hoare’s “retirement” from Oxford (the word is in quotes because he has had several further productive careers since). The organizers were Jim Woodcock, Bill Roscoe and Jim Davies. The proceedings are available as Millenial Perspectives in Computer Science, MacMillan Education UK, edited by Davies, Roscoe and Woodcock. The Symposium was a milestone event.

As part of a recent conversation on something else, YuQian Zhou(who was also there) sent me a group photo from the event, which I did not know even existed. I am including it below; it is actually a photo of a paper photo but the resolution is good. It is a fascinating gallery of outstanding people in programming and verification. (How many Turing award winners can you spot? I see 7.)

Many thanks to YuQian Zhou, Jim Woodcock and Bill Roscoe for insights into the picture in discussions of the past two weeks.

photo

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Statement Considered Harmful

I harbor no illusion about the effectiveness of airing this particular pet peeve; complaining about it has about the same chance of success as protesting against split infinitives or music in restaurants. Still, it is worth mentioning that the widespread use of the word “statement” to denote a programming language element, such as an assignment, that directs a computer to perform some change, is misleading. “Instruction” is the better term.

A “statement” is “something stated, such as a single declaration or remark, or a report of fact or opinions” (Merriam-Webster).

Why does it matter? The use of “statement” to mean “instruction” obscures a fundamental distinction of software engineering: the duality between specification and implementation. Programming produces a solution to a problem; success requires expressing both the problem, in the form of a specification, and the devised solution, in the form of an implementation. It is important at every stage to know exactly where we stand: on the problem side (the “what”) or the solution side (the “how”). In his famous Goto Statement Considered Harmful of 1968, Dijkstra beautifully characterized this distinction as the central issue of programming:

Our intellectual powers are rather geared to master static relations and our powers to visualize processes evolving in time are relatively poorly developed. For that reason we should do (as wise programmers aware of our limitations) our utmost to shorten the conceptual gap between the static program and the dynamic process, to make the correspondence between the program (spread out in text space) and the process (spread out in time) as trivial as possible.

Software verification, whether conducted through dynamic means (testing) or static techniques (static analysis, proofs of correctness), relies on having separately expressed both a specification of the intent and a proposed implementation intended to realize that intent. They have to remain distinct; otherwise we cannot even define what it means that the program should be correct (correct with respect to what?), and even less what it means to validate the program (validate it against what?).

In many approaches to verification, the properties against which we validate programs are called assertions. An assertion expresses a property that should hold at some point of program execution. For example, after the assignment instruction a := b + 1, the assertion ab will hold. This notion of assertion is used both in testing frameworks, such as JUnit for Java or PyUnit for Python, and in program proving frameworks; see, for example, the interactive Web-based version of the AutoProof program-proving framework for Eiffel at autoproof.sit.org, and of course the entire literature on axiomatic (Floyd-Hoare-Dijkstra-style) verification.

The difference between the instruction and the assertion is critical: a := b + 1 tells the computer to do something (change the value of a), as emphasized here by the “:=” notation for assignment; ab does not direct the computer or the computation to do anything, but simply states a property that should hold at a certain stage of the computation if everything went fine so far.

In the second case, the word “states” is indeed appropriate: an assertion states a certain property. The expression of that property, ab, is a “statement” in the ordinary English sense of the term. The command to the computer, a := b + 1, is an instruction whose effect is to ensure the satisfaction of the statement ab. So if we use the word “statement” at all, we should use it to mean an assertion, not an instruction.

If we start calling instructions “statements” (a usage that Merriam-Webster grudgingly accepts in its last entry for the term, although it takes care to define it as “an instruction in a computer program,” emphasis added), we lose this key distinction.

There is no reason for this usage, however, since the word “instruction” is available, and entirely appropriate.

So, please stop saying “an assignment statement” or “a print statement“; say “an assignment instruction” and so on.

Maybe you won’t, but at least you have been warned.

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

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“Object Success” now available

A full, free online version of Object Success
(1995)

success_cover

 

I am continuing the process of releasing some of my earlier books. Already available: Introduction to the Theory of Programming Languages (see here) and Object-Oriented Software Construction, 2nd edition (see here). The latest addition is Object Success, a book that introduced object technology to managers and more generally emphasized the management and organizational consequences of OO ideas.

The text (3.3 MB) is available here for download.

Copyright notice: The text is not in the public domain. It is copyrighted material (© Bertrand Meyer, 1995, 2023), made available free of charge on the Web for the convenience of readers, with the permission of the original publisher (Prentice Hall, now Pearson Education, Inc.). You are not permitted to copy it or redistribute it. Please refer others to the present version at bertrandmeyer.com/success.

(Please do not bookmark or share the above download link as it may change, but use the present page: https:/bertrandmeyer.com/success.) The text is republished identically, with minor reformatting and addition of some color. (There is only one actual change, a mention of the evolution of hardware resources, on page 136, plus a reference to a later book added to a bibliography section on page 103.) This electronic version is fully hyperlinked: clicking entries in the table of contents and index, and any element in dark red such as the page number above, will take you to the corresponding place in the text.

The book is a presentation of object technology for managers and a discussion of management issues of modern projects. While it is almost three decades old and inevitably contains some observations that will sound naïve  by today’s standards, I feel  it retains some of its value. Note in particular:

  • The introduction of a number of principles that went radically against conventional software engineering wisdom and were later included in agile methods. See Agile! The Good, the Hype and the Ugly, Springer, 2014, book page at agile.ethz.ch.
  • As an important example, the emphasis on the primacy of code. Numerous occurrences of the argument throughout the text. (Also, warnings about over-emphasizing analysis, design and other products, although unlike “lean development” the text definitely does not consider them to be “waste”. See the “bubbles and arrows of outrageous fortune”, page 80.)
  • In the same vein, the emphasis on incremental development.
  • Yet another agile-before-agile principle: Less-Is-More principle (in “CRISIS REMEDY”, page 133).
  • An analysis of the role of managers (chapters 7 to 9) which remains largely applicable, and I believe more realistic than the agile literature’s reductionist view of managers.
  • A systematic analysis of what “prototyping” means for software (chapter 4), distinguishing between desirable and less good forms.
  • Advice on how to salvage projects undergoing difficulties or crises (chapters 7 and 9).
  • A concise exposition of OO concepts (chapter 1 and appendix).
  • A systematic discussion of software lifecycle models (chapter 3), including the “cluster model”. See new developments on this topic in my recent “Handbook of Requirements and Business Analysis”, Springer, 2022, book page at bertrandmeyer.com/requirements.
  • More generally, important principles from which managers (and developers) can benefit today just as much as at the time of publication.

The download link again (3.3 MB): here it is.

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Logical beats sequential

Often,  “we do this and then we do that” is just a lazy way of stating “to do that, we must have achieved this.” The second form is more general than the first, since there may be many things you can “do” to achieve a certain condition.

The extra generality is welcome for software requirements, which should describe essential properties without over-specifying, in particular without prescribing a specific ordering of operations  when it is only one possible sequence among several, thereby restricting the flexibility of designers and implementers.

This matter of logical versus sequential constraints is at the heart of the distinction between scenario-based techniques — use cases, user stories… — and object-oriented requirements. This article analyzes the distinction. It is largely extracted from my recent textbook, the Handbook of Requirements and Business Analysis [1], which contains a more extensive discussion.

1. Scenarios versus OO

Scenario techniques, most significantly use cases and user stories, have become dominant in requirements. They obviously fill a need and are intuitive to many people. As a general requirement technique, however, they lack abstraction. Assessed against object-oriented requirements techniques, they suffer from the same limitations as procedural (pre-OO)  techniques against their OO competitors in the area of design and programming. The same arguments that make object technology subsume non-OO approaches in those areas transpose to requirements.

Scenario techniques describe system properties in terms of a particular sequence of interactions with the system. A staple example of a use case is ordering a product through an e-commerce site, going through a number of steps. In contrast, an OO specification presents a certain number of abstractions and operations on them, chracterized by their logical properties. This description may sound vague, so we move right away to examples.

2. Oh no, not stacks again

Yes, stacks. This example is rather computer-sciency so it is not meant to convince anyone but just to explain the ideas. (An example more similar to what we deal with in the requirements of industry projects is coming next.)

A stack is a LIFO (Last-In, First-Out) structure. You insert and remove elements at the same end.

 

Think of a stack of plates, where you can deposit one plate at a time, at the top, and retrieve one plate at a time, also at the top. We may call the two operations put and remove. Both are commands (often known under the alternative names push and pop). We will also use an integer query count giving the number of elements.

Assume we wanted to specify the behavior of a stack through use cases. Possible use cases (all starting with an empty stack) are:

/1/

put
put ; put
put ; put ; put       
— etc.: any number of successive put (our stacks are not bounded)

put ; remove
put ; put ; remove
put ; put ; remove ; remove
put ; put ; remove ; remove ; put ; remove

We should also find a way to specify that the system does not support such use cases as

/2/

remove ; put

or even just

/3/

remove

We could keep writing such use cases forever — some expressing normal sequences of operations, others describing erroneous cases — without capturing the fundamental rule that at any stage, the number of put so far has to be no less than the number of remove.

A simple way to capture this basic requirement is through logical constraints, also known as contracts, relying on assertions: preconditions which state the conditions under which an operation is permitted, and postconditions which describe properties of its outcome. In the example we can state that:

  • put has no precondition, and the postcondition

          count = old count + 1

using the old notation to refer to the value of an expression before the operation (here, the postcondition states that put increases count by one).

  • remove has the precondition

count > 0

and the postcondition

count = old count – 1

since it is not possible to remove an element from an empty stack. More generally the LIFO discipline implies that we cannot remove more than we have put.(Such illegal usage sequences are sometimes called “misuse cases.”)

(There are other properties, but the ones just given suffice for this discussion.)

The specification states what can be done with stacks (and what cannot) at a sufficiently high level of abstraction to capture all possible use cases. It enables us to keep track of the value of count in the successive steps of a use case; it tells us for example that all the use cases under /1/ above observe the constraints: with count starting at 0, taking into account the postconditions of put and remove, the precondition of every operation will be satisfied prior to all of its calls. For /2/ and /3/ that is not the case, so we know that these use cases are incorrect.

Although this example covers a data structure, not  requirements in the general sense, it illustrates how logical constraints are more general than scenarios:

  • Use cases, user stories and other  forms of scenario only describe specific instances of behavior.
  • An OO model with contracts yields a more abstract specification, to which individual scenarios can be shown to conform, or not.

3. Avoiding premature ordering decisions

As the stack example illustrates, object-oriented specifications stay away from premature time-order decisions by focusing on object types (classes) and their operations (queries and commands), without making an early commitment to the order of executing these operations.

In the book, I use in several places a use-case example from one of the best books about use cases (along with Ivar Jacobson’s original one of course): Alistair Cockburn’s Writing Effective Use Cases (Pearson Education, 2001). A simplified form of the example is:

1. A reporting party who is aware of the event registers a loss to the insurance company.

2. A clerk receives and assigns claim to a claims agent.

3. The assigned claims adjuster:

3.1 Conducts an investigation.
3.2 Evaluates damages.
3.3 Sets reserves.
3.4 Negotiates the claim.
3.5 Resolves the claim and closes it.

(A reserve in the insurance business is an amount that an insurer, when receiving a claim, sets aside as to cover the financial liability that may result from the claim.)

As a specification, this scenario is trying to express useful things; for example, you must set reserves before starting to negotiate the claim. But it expresses them in the form of a strict sequence of operations, a temporal constraint which does not cover the wide range of legitimate scenarios. As in the stack example, describing a few such scenarios is helpful as part of requirements elicitation, but to specify the resulting requirements it is more effective to state the logical constraints.

Here is a sketch (in Eiffel) of how a class INSURANCE_CLAIM could specify them in the form of contracts. Note the use of require to introduce a precondition and ensure for postconditions.

class INSURANCE_CLAIM feature

        — Boolean queries (all with default value False):
    is_investigated, is_evaluated, is_reserved,is_agreed,is_imposed, is_resolved:

BOOLEAN

    investigate
                — Conduct investigation on validity of claim. Set is_investigated.
        deferred
        ensure
            is_investigated
        end

    evaluate
                — Assess monetary amount of damages.
        require
            is_investigated
        deferred
        ensure
            is_evaluated
            — Note: is_investigated still holds (see the invariant at the end of the class text).
        end

    set_reserve
                — Assess monetary amount of damages. Set is_reserved.
        require
            is_investigated
            — Note: we do not require is_evaluated.
        deferred
        ensure
            is_reserved
        end
 

    negotiate
                — Assess monetary amount of damages. Set is_agreed only if negotiation
                — leads to an agreement with the claim originator.
        require
                   is_reserved
is_evaluated   
                   

        deferred
        ensure
            is_reserved
            — See the invariant for is_evaluated and is_investigated.
        end

    impose (amount: INTEGER)
                — Determine amount of claim if negotiation fails. Set is_imposed.
        require
            not is_agreed
            is_reserved
        deferred
        ensure
            is_imposed
        end

    resolve
                — Finalize handling of claim. Set is_resolved.
        require
            is_agreed or is_imposed
        deferred
        ensure
            is_resolved
        end

invariant                    — “⇒” is logical implication.

is_evaluated is_investigated
is_reserved 
is_evaluated
is_resolved
is_agreed or is_imposed
is_agreed
is_evaluated
is_imposed
is_evaluated
is_imposed
not is_agreed

                          — Hence, by laws of logic, is_agreed not is_imposed

end

Notice the interplay between the preconditions, postconditions and class invariant, and the various boolean-valued queries they involve (is_investigated, is_evaluated, is_reserved…). You can specify a strict order of operations o1, o2 …, as in a use case, by having a sequence of assertions pi such that operation oi has the contract clauses require pi and ensure pi+1; but assertions also enable you to specify a much broader range of allowable orderings as all acceptable.
The class specification as given is only a first cut and leaves many aspects untouched. It will be important in practice, for example, to include a query payment describing the amount to be paid for the claim; then impose has the postcondition payment = amount, and negotiate sets a certain amount for payment.
Even in this simplified form, the specification includes a few concepts that the original use case left unspecified, in particular the notion of imposing a payment (through the command impose) if negotiation fails. Using a logical style typically uncovers such important questions and provides a framework for answering them, helping to achieve one of the principal goals of requirements engineering.

4. Logical constraints are more general than sequential orderings

The specific sequence of actions described in the original use case (“main success scenario”) is compatible with the logical constraints: you can check that in the sequence

investigate
evaluate
set_reserve
negotiate
resolve

the postcondition of each step implies the precondition of the next one (the first has no precondition). In other words, the temporal specification satisfies the logical one. But you can also see that prescribing this order is a case of overspecification: other orderings also satisfy the logical specification. It may be possible for example — subject to confirmation by Subject-Matter Experts — to change the order of evaluate and set_reserve, or to perform these two operations in parallel.

The specification does cover the fundamental sequencing constraints; for example, the pre- and postcondition combinations imply that investigation must come before evaluation and resolution must be preceded by either negotiation or imposition. But they avoid the non-essential constraints which, in the use case, were only an artifact of the sequential style of specification, not a true feature of the problem.

The logical style is also more conducive to conducting a fruitful dialogue with domain experts and stakeholders:

  • With a focus on use cases, the typical question from a requirements engineer (business analyst) is “do you do A before doing B?” Often the answer will be contorted, as in “usually yes, but only if C, oh and sometimes we might start with B if D holds, or we might work on A and B in parallel…“, leading to vagueness and to more complicated requirements specifications.
  • With logic-based specifications, the two fundamental question types are: “what conditions do you need before doing B?” and “does doing A ensure condition C?”. They force stakeholders to assess their own practices and specify precisely the relations between operations of interest.

5. What use for scenarios?

Use-cases and more generally scenarios, while more restrictive than logical specifications, remain important as complements to specifications. They serve as both input and output to more abstract requirements specifications (such as OO specifications with contracts):

  • As input to requirements: initially at least, stakeholders and Subject-Matter Experts often find it intuitive to describe typical system interactions, and their own activities, in the form of scenarios. Collecting such scenarios is an invaluable requirements elicitation technique. The requirements engineer must remember that any such scenario is just one example walk through the system, and must abstract from these examples to derive general logical rules.
  • As output from requirements: from an OO specification with its contracts, the requirements engineers can produce valid use cases. “Valid” means that the operation at every step satisfies the applicable precondition, as a consequence of the previous steps’ postconditions and of the class invariant. The requirements engineers can then submit these use cases to the SMEs and through them to stakeholders to confirm that they make sense, update the logical conditions if they do not (to rule out bad use cases), and check the results they are expected to produce.

6. Where do scenarios fit?

While many teams will prefer to write scenarios (for the purposes just described) in natural language, it is possible to go one step further and, in an object-oriented approach to requirements, gather scenarios in classes. But that point exceeds the scope of the present sketch. We will limit ourselves here to the core observation: logical constraints subsume sequential specifications; you can deduce the ltter from the former, but not the other way around; and focusing on abstract logical specifications leads to a better understanding of the requirements.

Reference

Bertrand Meyer: Handbook of Requirements and Business Analysis, Springer, 2022. See the book page with sample chapters and further material here.

Recycled(This article was first published on the Communications of the ACM blog.)

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New book: the Requirements Handbook

cover

I am happy to announce the publication of the Handbook of Requirements and Business Analysis (Springer, 2022).

It is the result of many years of thinking about requirements and how to do them right, taking advantage of modern principles of software engineering. While programming, languages, design techniques, process models and other software engineering disciplines have progressed considerably, requirements engineering remains the sick cousin. With this book I am trying to help close the gap.

pegsThe Handbook introduces a comprehensive view of requirements including four elements or PEGS: Project, Environment, Goals and System. One of its principal contributions is the definition of a standard plan for requirements documents, consisting of the four corresponding books and replacing the obsolete IEEE 1998 structure.

The text covers both classical requirements techniques and novel topics such as object-oriented requirements and the use of formal methods.

The successive chapters address: fundamental concepts and definitions; requirements principles; the Standard Plan for requirements; how to write good requirements; how to gather requirements; scenario techniques (use cases, user stories); object-oriented requirements; how to take advantage of formal methods; abstract data types; and the place of requirements in the software lifecycle.

The Handbook is suitable both as a practical guide for industry and as a textbook, with over 50 exercises and supplementary material available from the book’s site.

You can find here a book page with the preface and sample chapters.

To purchase the book, see the book page at Springer and the book page at Amazon US.

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Introduction to axiomatic semantics

itplI have released for general usage the chapter on axiomatic semantics of my book Introduction to the Theory of Programming Languages. It’s old but I think it is still a good introduction to the topic. It explains:

  • The notion of theory (with a nice — I think — example borrowed from an article by Luca Cardelli: axiomatizing types in lambda calculus).
  • How to axiomatize a programming language.
  • The notion of assertion.
  • Hoare-style pre-post semantics, dealing with arrays, loop invariants etc.
  • Dijkstra’s calculus of weakest preconditions.
  • Non-determinism.
  • Dealing with routines and recursion.
  • Assertion-guided program construction (in other words, correctness by construction), design heuristics (from material in an early paper at IFIP).
  • 26 exercises.

The text can be found at

https://se.inf.ethz.ch/~meyer/publications/theory/09-axiom.pdf

It remains copyrighted but can be used freely. It was available before since I used it for courses on software verification but the link from my publication page was broken. Also, the figures were missing; I added them back.

I thought I only had the original (troff) files, which I have no easy way to process today, but just found PDFs for all the chapters, likely produced a few years ago when I was still able to put together a working troff setup. They are missing the figures, which I have to scan from a printed copy and reinsert. I just did it for the chapter on mathematical notations, chapter 2, which you can find at https://se.inf.ethz.ch/~meyer/publications/theory/02-math.pdf. If there is interest I will release all chapters (with corrections of errata reported by various readers over the years).

The chapters of the book are:

  • (Preface)
  1. Basic concepts
  2. Mathematical background (available through the link above).
  3. Syntax (introduces formal techniques for describing syntax, included a simplified BNF).
  4. Semantics: the main approaches (overview of the techniques described in detail in the following chapters).
  5. Lambda calculus.
  6. Denotational semantics: fundamentals.
  7. Denotational semantics: language features (covers denotational-style specifications of records, arrays, input/output etc.).
  8. The mathematics of recursion (talks in particular about iterative methods and fixpoints, and the bottom-up interpretation of recursion, based on work by Gérard Berry).
  9. Axiomatic semantics (available through the link above).
  10. Complementary semantic definitions (establishing a clear relationship between different specifications, particular axiomatic and denotational).
  • Bibliography

Numerous exercises are included. The formal models use throughout a small example language called Graal (for “Great Relief After Ada Lessons”).  The emphasis is on understanding programming and programming languages through simple mathematical models.

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OOSC-2 available online (officially)

My book Object-Oriented Software Construction, 2nd edition (see the Wikipedia page) has become hard to get. There are various copies floating around the Web but they often use bad typography (wrong colors) and are unauthorized.

In response to numerous requests and in anticipation of the third edition I have been able to make it available electronically (with the explicit permission of the original publisher).

You can find the link on another page on this site. (In sharing or linking please use that page, not the URL of the actual PDF which might change.)

I hope having the text freely available proves useful.

 

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Publication announcement: survey on requirements techniques, formal and non-formal

There is a new paper out, several years in the making:

The Role of Formalism in System Requirements
Jean-Michel Bruel, Sophie Ebersold, Florian Galinier, Manuel Mazzara, Alexander Naumchev, Bertrand Meyer
Computing Surveys (ACM), vol. 54, no. 5, June 2021, pages 1-36
DOI: https://doi.org/10.1145/3448975
Preprint available here.

The authors are from the Schaffhausen Institute of Technology in Switzerland, the University of Toulouse in France and Innopolis University in Russia. We make up a cross-institutional (and unofficial) research group which has for several years now been working on improving the state of software requirements, with both an engineering perspective and an interest in taking advantage of formal methods.

The article follows this combined formal-informal approach by reviewing the principal formal methods in requirements but also taking into consideration non-formal ones — including techniques widely used in industry, such as DOORS — and studying how they can be used in a more systematic way. It uses a significant example (a “Landing Gear System” or LGS for aircraft) to compare them and includes extensive tables comparing the approaches along a number of systematic criteria.

Here is the abstract:

A major determinant of the quality of software systems is the quality of their requirements, which should be both understandable and precise. Most requirements are written in natural language, which is good for understandability but lacks precision.

To make requirements precise, researchers have for years advocated the use of mathematics-based notations and methods, known as “formal.” Many exist, differing in their style, scope, and applicability.

The present survey discusses some of the main formal approaches and compares them to informal methods.The analysis uses a set of nine complementary criteria, such as level of abstraction, tool availability, and traceability support. It classifies the approaches into five categories based on their principal style for specifying requirements: natural-language, semi-formal, automata/graphs, mathematical, and seamless (programming-language-based). It includes examples from all of these categories, altogether 21 different approaches, including for example SysML, Relax, Eiffel, Event-B, and Alloy.

The review discusses a number of open questions, including seamlessness, the role of tools and education, and how to make industrial applications benefit more from the contributions of formal approaches.

For me, of course, this work is the continuation of a long-running interest in requirements and specifications and how to express them using the tools of mathematics, starting with a 1985 paper, still being cited today, with a strikingly similar title: On Formalism in Specifications.

Trivia: the “response to referees” (there were no fewer than eight of them!) after the first review took up 85 pages. Maybe not for the Guinness Book, but definitely a personal record. (And an opportunity to thank the referees for detailed comments that considerably helped shape the final form of the paper.)

Correction (20 July 2021): I just noted that I had forgotten to list myself among the authors! Not a sign of modesty (I don’t have any), more of absent-mindedness. Now corrected.

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Some contributions

Science progresses through people taking advantage of others’ insights and inventions. One of the conditions that makes the game possible is that you acknowledge what you take. For the originator, it is rewarding to see one’s ideas reused, but frustrating when that happens without acknowledgment, especially when you are yourself punctilious about citing your own sources of inspiration.

I have started to record some concepts that are widely known and applied today and which I believe I originated in whole or in part, whether or not their origin is cited by those who took them. The list below is not complete and I may update it in the future. It is not a list of ideas I contributed, only of those fulfilling two criteria:

  • Others have built upon them.  (If there is an idea that I think is great but no one paid attention to it, the list does not include it.)
  • They have gained wide visibility.

There is a narcissistic aspect to this exercise and if people want to dismiss it as just showing I am full of myself so be it. I am just a little tired of being given papers to referee that state that genericity was invented by Java, that no one ever thought of refactoring before agile methods, and so on. It is finally time to state some facts.

Facts indeed: I back every assertion by precise references. So if I am wrong — i.e. someone preceded me — the claims of precedence can be refuted; if so I will update or remove them. All articles by me cited in this note are available (as downloadable PDFs) on my publication page. (The page is up to date until 2018; I am in the process of adding newer publications.)

Post-publication note: I have started to receive some comments and added them in a Notes section at the end; references to those notes are in the format [A].

Final disclaimer (about the narcissistic aspect): the exercise of collecting such of that information was new for me, as I do not usually spend time reflecting on the past. I am much more interested in the future and definitely hope that my next contributions will eclipse any of the ones listed below.

Programming concepts: substitution principle

Far from me any wish to under-represent the seminal contributions of Barbara Liskov, particularly her invention of the concept of abstract data type on which so much relies. As far as I can tell, however, what has come to be known as the “Liskov Substitution Principle” is essentially contained in the discussion of polymorphism in section 10.1 of in the first edition (Prentice Hall, 1988) of my book Object-Oriented Software Construction (hereafter OOSC1); for example, “the type compatibility rule implies that the dynamic type is always a descendant of the static type” (10.1.7) and “if B inherits from A, the set of objects that can be associated at run time with an entity [generalization of variable] includes instances of B and its descendants”.

Perhaps most tellingly, a key aspect of the substitution principle, as listed for example in the Wikipedia entry, is the rule on assertions: in a proper descendant, keep the invariant, keep or weaken the precondition, keep or strengthen the postcondition. This rule was introduced in OOSC1, over several pages in section 11.1. There is also an extensive discussion in the article Eiffel: Applying the Principles of Object-Oriented Design published in the Journal of Systems and Software, May 1986.

The original 1988 Liskov article cited (for example) in the Wikipedia entry on the substitution principle says nothing about this and does not in fact include any of the terms “assertion”, “precondition”, “postcondition” or “invariant”. To me this absence means that the article misses a key property of substitution: that the abstract semantics remain the same. (Also cited is a 1994 Liskov article in TOPLAS, but that was many years after OOSC1 and other articles explaining substitution and the assertion rules.)

Liskov’s original paper states that “if for each object o1 of type S there is an object o2 of type T such that for all programs P defined in terms of T, the behavior of P is unchanged when o1 is substituted for oz, then S is a subtype of T.” As stated, this property is impossible to satisfy: if the behavior is identical, then the implementations are the same, and the two types are identical (or differ only by name). Of course the concrete behaviors are different: applying the operation rotate to two different figures o1 and o2, whose types are subtypes of FIGURE and in some cases of each other, will trigger different algorithms — different behaviors. Only with assertions (contracts) does the substitution idea make sense: the abstract behavior, as characterized by preconditions, postconditions and the class invariants, is the same (modulo respective weakening and strengthening to preserve the flexibility of the different version). Realizing this was a major step in understanding inheritance and typing.

I do not know of any earlier (or contemporary) exposition of this principle and it would be normal to get the appropriate recognition.

Software design: design patterns

Two of the important patterns in the “Gang of Four” Design Patterns book (GoF) by Gamma et al. (1995) are the Command Pattern and the Bridge Pattern. I introduced them (under different names) in the following publications:

  • The command pattern appears in OOSC1 under the name “Undo-Redo” in section 12.2. The solution is essentially the same as in GoF. I do not know of any earlier exposition of the technique. See also notes [B] and [C].
  • The bridge pattern appears under the name “handle technique” in my book Reusable Software: The Base Component Libraries (Prentice Hall, 1994). It had been described several years earlier in manuals for Eiffel libraries. I do not know of an earlier reference. (The second edition of Object-Oriented Software Construction — Prentice Hall, 1997, “OOSC2” –, which also describes it, states that a similar technique is described in an article by Josef Gil and Ricardo Szmit at the TOOLS USA conference in the summer of 1994, i.e. after the publication of Reusable Software.)

Note that it is pointless to claim precedence over GoF since that book explicitly states that it is collecting known “best practices”, not introducing new ones. The relevant questions are: who, pre-GoF, introduced each of these techniques first; and which publications does the GoF cites as “prior art”  for each pattern. In the cases at hand, Command and Bridge, it does not cite OOSC1.

To be concrete: unless someone can point to an earlier reference, then anytime anyone anywhere using an interactive system enters a few “CTRL-Z” to undo commands, possibly followed by some “CTRL-Y” to redo them (or uses other UI conventions to achieve these goals), the software most likely relying on a technique that I first described in the place mentioned above.

Software design: Open-Closed Principle

Another contribution of OOSC1 (1988), section 2.3, reinforced in OOSC2 (1997) is the Open-Closed principle, which explained one of the key aspects of inheritance: the ability to keep a module both closed (immediately usable as is) and open to extension (through inheritance, preserving the basic semantics. I am mentioning this idea only in passing since in this case my contribution is usually recognized, for example in the Wikipedia entry.

Software design: OO for reuse

Reusability: the Case for Object-Oriented Design (1987) is, I believe, the first publication that clearly explained why object-oriented concepts were (and still are today — in Grady Booch’s words, “there is no other game in town”) the best answer to realize the goal of software construction from software components. In particular, the article:

  • Explains the relationship between abstract data types and OO programming, showing the former as the theoretical basis for the latter. (The CLU language at MIT originated from Liskov’s pioneering work on abstract data types, but was not OO in the full sense of the term, missing in particular a concept of inheritance.)
  • Shows that reusability implies bottom-up development. (Top-down refinement was the mantra at the time, and promoting bottom-up was quite a shock for many people.)
  • Explains the role of inheritance for reuse, as a complement to Parnas’s interface-based modular construction with information hiding.

Software design: Design by Contract

The contribution of Design by Contract is one that is widely acknowledged so I don’t have any point to establish here — I will just recall the essentials. The notion of assertion goes back to the work of Floyd, Hoare and Dijkstra in the sixties and seventies, and correctness-by-construction to Dijktra, Gries and Wirth, but Design by Contract is a comprehensive framework providing:

  • The use of assertions in an object-oriented context. (The notion of class invariant was mentioned in a paper by Tony Hoare published back in 1972.)
  • The connection of inheritance with assertions (as sketched above). That part as far as I know was entirely new.
  • A design methodology for quality software: the core of DbC.
  • Language constructs carefully seamed into the fabric of the language. (There were precedents there, but in the form of research languages such as Alphard, a paper design only, not implemented, and Euclid.)
  • A documentation methodology.
  • Support for testing.
  • Support for a consistent theory of exception handling (see next).

Design by Contract is sometimes taken to mean simply the addition of a few assertions here and there. What the term actually denotes is a comprehensive methodology with all the above components, tightly integrated into the programming language. Note in particular that preconditions and postconditions are not sufficient; in an OO context class invariants are essential.

Software design: exceptions

Prior to the Design by Contract work, exceptions were defined very vaguely, as something special you do outside of “normal” cases, but without defining “normal”. Design by Contract brings a proper perspective by defining these concepts precisely. This was explained in a 1987 article, Disciplined Exceptions ([86] in the list), rejected by ECOOP but circulated as a technical report; they appear again in detail in OOSC1 (sections 7.10.3 to 7.10.5).

Other important foundational work on exceptions, to which I know no real precursor (as usual I would be happy to correct any omission), addressed what happens to the outcome of an exception in a concurrent or distributed context. This work was done at ETH, in particular in the PhD theses  of B. Morandi and A. Kolesnichenko, co-supervised with S. Nanz. See the co-authored papers [345] and [363].

On the verification aspect of exceptions, see below.

Software design: refactoring

I have never seen a discussion of refactoring that refers to the detailed discussion of generalization in both of the books Reusable Software (1994, chapter 3) and Object Success (Prentice Hall, 1995, from page 122 to the end of chapter 6). These discussions describe in detail how, once a program has been shown to work, it should be subject to a posteriori design improvements. It presents several of the refactoring techniques (as they were called when the idea gained traction several years later), such as moving common elements up in the class hierarchy, and adding an abstract class as parent to concrete classes ex post facto.

These ideas are an integral part of the design methodology presented in these books (and again in OOSC2 a few later). It is beyond me why people would present refactoring (or its history, as in the Wikipedia entry on the topic) without referring to these publications, which were widely circulated and are available for anyone to inspect.

Software design: built-in documentation and Single-Product principle

Another original contribution was the idea of including documentation in the code itself and relying on tools to extract the documentation-only information (leaving implementation elements aside). The idea, described in detail in OOSC1 in 1988 (sections 9.4 and 9.5) and already mentioned in the earlier Eiffel papers, is that code should be self-complete, containing elements of various levels of abstraction; some of them describe implementation, but the higher-level elements describe specification, and are distinguished syntactically in such a way that tools can extract them to produce documentation at any desired level of abstraction.

The ideas were later applied through such mechanisms as JavaDoc (with no credit as far as I know). They were present in Eiffel from the start and the underlying principles, in particular the “Single Product principle” (sometimes “Self-Documentation principle”, and also generalized by J. Ostroff and R. Paige as “Single-Model principle”). Eiffel is the best realization of these principles thanks to:

  • Contracts (as mentioned above): the “contract view” of a class (called “short form” in earlier descriptions) removes the implementations but shows the relevant preconditions, postconditions and class invariants, given a precise and abstract specification of the class.
  • Eiffel syntax has a special place for “header comments”, which describe high-level properties and remain in the contract view.
  • Eiffel library class documentation has always been based on specifications automatically extracted from the actual text of the classes, guaranteeing adequacy of the documentation. Several formats are supported (including, from 1995 on, HTML, so that documentation can be automatically deployed on the Web).
  • Starting with the EiffelCase tool in the early 90s, and today with the Diagram Tool of EiffelStudio, class structures (inheritance and client relationships) are displayed graphically, again in an automatically extracted form, using either the BON or UML conventions.

One of the core benefits of the Single-Product principle is to guard against what some of my publications called the “Dorian Gray” syndrome: divergence of an implementation from its description, a critical problem in software because of the ease of modifying stuff. Having the documentation as an integral part of the code helps ensure that when information at some level of abstraction (specification, design, implementation) changes, the other levels will be updated as well.

Crucial in the approach is the “roundtripping” requirement: specifiers or implementers can make changes in any of the views, and have them reflected automatically in the other views. For example, you can graphically draw an arrow between two bubbles representing classes B and A in the Diagram Tool, and the code of B will be updated with “inherit A”; or you can add this Inheritance clause textually in the code of class B, and the diagram will be automatically updated with an arrow.

It is important to note how contrarian and subversive these ideas were at the time of their introduction (and still to some extent today). The wisdom was that you do requirements then design then implementation, and that code is a lowly product entirely separate from specification and documentation. Model-Driven Development perpetuates this idea (you are not supposed to modify the code, and if you do there is generally no easy way to propagate the change to the model.) Rehabilitating the code (a precursor idea to agile methods, see below) was a complete change of perspective.

I am aware of no precedent for this Single Product approach. The closest earlier ideas I can think of are in Knuth’s introduction of Literate Programming in the early eighties (with a book in 1984). As in the Single-product approach, documentation is interspersed with code. But the literate programming approach is (as presented) top-down, with English-like explanations progressively being extended with implementation elements. The Single Product approach emphasizes the primacy of code and, in terms of the design process, is very much yoyo, alternating top-down (from the specification to the implementation) and bottom-up (from the implementation to the abstraction) steps. In addition, a large part of the documentation, and often the most important one, is not informal English but formal assertions. I knew about Literate Programming, of course, and learned from it, but Single-Product is something else.

Software design: from patterns to components

Karine Arnout’s thesis at ETH Zurich, resulting in two co-authored articles ([255] and [257], showed that contrary to conventional wisdom a good proportion of the classical design patterns, including some of the most sophisticated, can be transformed into reusable components (indeed part of an Eiffel library). The agent mechanism (see below) was instrumental in achieving that result.

Programming, design and specification concepts: abstract data types

Liskov’s and Zilles’s ground-breaking 1974 abstract data types paper presented the concepts without a mathematical specification, using programming language constructs instead. A 1976 paper (number [3] in my publication list, La Description des Structures de Données, i.e. the description of data structures) was as far as I know one of the first to present a mathematical formalism, as  used today in presentations of ADTs. John Guttag was taking a similar approach in his PhD thesis at about the same time, and went further in providing a sound mathematical foundation, introducing in particular (in a 1978 paper with Jim Horning) the notion of sufficient completeness, to which I devoted a full article in this blog  (Are My Requirements Complete?) about a year ago. My own article was published in a not very well known journal and in French, so I don’t think it had much direct influence. (My later books reused some of the material.)

The three-level description approach of that article (later presented in English for an ACM workshop in the US in 1981, Pingree Park, reference [28]) is not well known but still applicable, and would be useful to avoid frequent confusions between ADT specifications and more explicit descriptions.

When I wrote my 1976 paper, I was not aware of Guttag’s ongoing work (only of the Liskov and Zilles paper), so the use of a mathematical framework with functions and predicates on them was devised independently. (I remember being quite happy when I saw what the axioms should be for a queue.) Guttag and I both gave talks at a workshop organized by the French programming language interest group in 1977 and it was fun to see that our presentations were almost identical. I think my paper still reads well today (well, if you read French). Whether or not it exerted direct influence, I am proud that it independently introduced the modern way of thinking of abstract data types as characterized by mathematical functions and their formal (predicate calculus) properties.

Language mechanisms: genericity with inheritance

Every once in a while I get to referee a paper that starts “Generics, as introduced in Java…” Well, let’s get some perspective here. Eiffel from its introduction in 1985 combined genericity and inheritance. Initially, C++ users and designers claimed that genericity was not needed in an OO context and the language did not have it; then they introduced template. Initially, the designers of Java claimed (around 1995) that genericity was not needed, and the language did not have it; a few years later Java got generics. Initially, the designers of C# (around 1999) claimed that genericity was not needed, and the language did not have it; a few years later C# and .NET got generics.

Genericity existed before Eiffel of course; what was new was the combination with inheritance. I had been influenced by work on generic modules by a French researcher, Didier Bert, which I believe influenced the design of Ada as well; Ada was the language that brought genericity to a much broader audience than the somewhat confidential languages that had such a mechanism before. But Ada was not object-oriented (it only had modules, not classes). I was passionate about object-oriented programming (at a time when it was generally considered, by the few people who had heard of it as an esoteric, academic pursuit). I started — in the context of an advanced course I was teaching at UC Santa Barbara — an investigation of how the two mechanisms relate to each other. The results were a paper at the first OOPSLA in 1986, Genericity versus Inheritance, and the design of the Eiffel type system, with a class mechanism, inheritance (single and multiple), and genericity, carefully crafted to complement each other.

With the exception of a Trellis-Owl, a  design from Digital Equipment Corporation also presented at the same OOPSLA (which never gained significant usage), there were no other OO languages with both mechanisms for several years after the Genericity versus Inheritance paper and the implementation of genericity with inheritance in Eiffel available from 1986 on. Eiffel also introduced, as far as I know, the concept of constrained genericity, the second basic mechanism for combining genericity with inheritance, described in Eiffel: The Language (Prentice Hall, 1992, section 10.8) and discussed again in OOSC2 (section 16.4 and throughout). Similar mechanisms are present in many languages today.

It was not always so. I distinctly remember people bringing their friends to our booth at some conference in the early nineties, for the sole purpose of having a good laugh with them at our poster advertising genericity with inheritance. (“What is this thing they have and no one else does? Generi-sissy-tee? Hahaha.”). A few years later, proponents of Java were pontificating that no serious language needs generics.

It is undoubtedly part of of the cycle of invention (there is a Schopenhauer citation on this, actually the only thing from Schopenhauer’s philosophy that I ever understood [D]) that people at some point will laugh at you; if it did brighten their day, why would the inventor deny them one of the little pleasures of life? But in terms of who laughs last, along the way C++ got templates, Java got generics, C# finally did too, and nowadays all typed OO languages have something of the sort.

Language mechanisms: multiple inheritance

Some readers will probably have been told that multiple inheritance is a bad thing, and hence will not count it as a contribution, but if done properly it provides a major abstraction mechanism, useful in many circumstances. Eiffel showed how to do multiple inheritance right by clearly distinguishing between features (operations) and their names, defining a class as a finite mapping between names and features, and using renaming to resolve any name clashes.

Multiple inheritance was made possible by an implementation innovation: discovering a technique (widely imitated since, including in single-inheritance contexts) to implement dynamic binding in constant time. It was universally believed at the time that multiple inheritance had a strong impact on performance, because dynamic binding implied a run-time traversal of the class inheritance structure, already bad enough for single inheritance where the structure is a tree, but prohibitive with multiple inheritance for which it is a directed acyclic graph. From its very first implementation in 1986 Eiffel used what is today known as a virtual table technique which guarantees constant-time execution of routine (method) calls with dynamic binding.

Language mechanisms: safe GC through strong static typing

Simula 67 implementations did not have automatic garbage collection, and neither had implementations of C++. The official excuse in the C++ case was methodological: C programmers are used to exerting manual control of memory usage. But the real reason was a technical impossibility resulting from the design of the language: compatibility with C precludes the provision of a good GC.

More precisely, of a sound and complete GC. A GC is sound if it will only reclaim unreachable objects; it is complete if it will reclaim all unreachable objects. With a C-based language supporting casts (e.g. between integers and pointers) and pointer arithmetic, it is impossible to achieve soundness if we aim at a reasonable level of completeness: a pointer can masquerade as an integer, only to be cast back into a pointer later on, but in the meantime the garbage collector, not recognizing it as a pointer, may have wrongly reclaimed the corresponding object. Catastrophe.

It is only possible in such a language to have a conservative GC, meaning that it renounces completeness. A conservative GC will treat as a pointer any integer whose value could possibly be a pointer (because it lies between the bounds of the program’s data addresses in memory). Then, out of precaution, the GC will refrain from reclaiming the objects at these addresses even if they appear unreachable.

This approach makes the GC sound but it is only a heuristics, and it inevitably loses completeness: every once in a while it will fail to reclaim some dead (unreachable) objects around. The result is a program with memory leaks — usually unacceptable in practice, particularly for long-running or continuously running programs where the leaks inexorably accumulate until the program starts thrashing then runs out of memory.

Smalltalk, like Lisp, made garbage collection possible, but was not a typed language and missed on the performance benefits of treating simple values like integers as a non-OO language would. Although in this case I do not at the moment have a specific bibliographic reference, I believe that it is in the context of Eiffel that the close connection between strong static typing (avoiding mechanisms such as casts and pointer arithmetic) and the possibility of sound and complete garbage collection was first clearly explained. Explained in particular around 1990 in a meeting with some of the future designers of Java, which uses a similar approach, also taken over later on by C#.

By the way, no one will laugh at you today for considering garbage collection as a kind of basic human right for programmers, but for a long time the very idea was quite sulfurous, and advocating it subjected you to a lot of scorn. Here is an extract of the review I got when I submitted the first Eiffel paper to IEEE Transactions on Software Engineering:

Systems that do automatic garbage collection and prevent the designer from doing his own memory management are not good systems for industrial-strength software engineering.

Famous last words. Another gem from another reviewer of the same paper:

I think time will show that inheritance (section 1.5.3) is a terrible idea.

Wow! I wish the anonymous reviewers would tell us what they think today. Needless to say, the paper was summarily rejected. (It later appeared in the Journal of Systems and Software — as [82] in the publication list — thanks to the enlightened views of Robert Glass, the founding editor.)

Language mechanisms: void safety

Void safety is a property of a language design that guarantees the absence of the plague of null pointer dereferencing.

The original idea came (as far as I know) from work at Microsoft Research that led to the design of a research language called C-omega; the techniques were not transferred to a full-fledged programming language. Benefiting from the existence of this proof of concept, the Eiffel design was reworked to guarantee void safety, starting from my 2005 ECOOP keynote paper (Attached Types) and reaching full type safety a few years later. This property of the language was mechanically proved in a 2016 ETH thesis by A. Kogtenkov.

Today all significant Eiffel development produces void-safe code. As far as I know this was a first among production programming languages and Eiffel remains the only production language to provide a guarantee of full void-safety.

This mechanism, carefully crafted (hint: the difficult part is initialization), is among those of which I am proudest, because in the rest of the programming world null pointer dereferencing is a major plague, threatening at any moment to crash the execution of any program that uses pointers of references. For Eiffel users it is gone.

Language mechanisms: agents/delegates/lambdas

For a long time, OO programming languages did not have a mechanism for defining objects wrapping individual operations. Eiffel’s agent facility was the first such mechanism or among the very first together the roughly contemporaneous but initially much more limited delegates of C#. The 1999 paper From calls to agents (with P. Dubois, M. Howard, M. Schweitzer and E. Stapf, [196] in the list) was as far as I know the first description of such a construct in the scientific literature.

Language mechanisms: concurrency

The 1993 Communications of the ACM paper on Systematic Concurrent Object-Oriented Programming [136] was certainly not the first concurrency proposal for OO programming (there had been pioneering work reported in particular in the 1987 book edited by Tokoro and Yonezawa), but it innovated in offering a completely data-race-free model, still a rarity today (think for example of the multi-threading mechanisms of dominant OO languages).

SCOOP, as it came to be called, was implemented a few years later and is today a standard part of Eiffel.

Language mechanisms: selective exports

Information hiding, as introduced by Parnas in his two seminal 1972 articles, distinguishes between public and secret features of a module. The first OO programming language, Simula 67, had only these two possibilities for classes and so did Ada for modules.

In building libraries of reusable components I realized early on that we need a more fine-grained mechanism. For example if class LINKED_LIST uses an auxiliary class LINKABLE to represent individual cells of a linked list (each with a value field and a “right” field containing a reference to another LINKABLE), the features of LINKABLE (such as the operation to reattach the “right” field) should not be secret, since LINKED_LIST needs them; but they should also not be generally public, since we do not want arbitrary client objects to mess around with the internal structure of the list. They should be exported selectively to LINKED_LIST only. The Eiffel syntax is simple: declare these operations in a clause of the class labeled “feature {LINKED_LIST}”.

This mechanism, known as selective exports, was introduced around 1989 (it is specified in full in Eiffel: The Language, from 1992, but was in the Eiffel manuals earlier). I think it predated the C++ “friends” mechanism which serves a similar purpose (maybe someone with knowledge of the history of C++ has the exact date). Selective exports are more general than the friends facility and similar ones in other OO languages: specifying a class as a friend means it has access to all your internals. This solution is too coarse-grained. Eiffel’s selective exports make it possible to define the specific export rights of individual operations (including attributes/fields) individually.

Language mechanisms and implementation: serialization and schema evolution

I did not invent serialization. As a student at Stanford in 1974 I had the privilege, at the AI lab, of using SAIL (Stanford Artificial Intelligence Language). SAIL was not object-oriented but included many innovative ideas; it was far ahead of its time, especially in terms of the integration of the language with (what was not yet called) its IDE. One feature of SAIL with which one could fall in love at first sight was the possibility of selecting an object and having its full dependent data structure (the entire subgraph of the object graph reached by following references from the object, recursively) stored into a file, for retrieval at the next section. After that, I never wanted again to live without such a facility, but no other language and environment had it.

Serialization was almost the first thing we implemented for Eiffel: the ability to write object.store (file) to have the entire structure from object stored into file, and the corresponding retrieval operation. OOSC1 (section 15.5) presents these mechanisms. Simula and (I think) C++ did not have anything of the sort; I am not sure about Smalltalk. Later on, of course, serialization mechanisms became a frequent component of OO environments.

Eiffel remained innovative by tackling the difficult problems: what happens when you try to retrieve an object structure and some classes have changed? Only with a coherent theoretical framework as provided in Eiffel by Design by Contract can one devise a meaningful solution. The problem and our solutions are described in detail in OOSC2 (the whole of chapter 31, particularly the section entitled “Schema evolution”). Further advances were made by Marco Piccioni in his PhD thesis at ETH and published in joint papers with him and M. Oriol, particularly [352].

Language mechanisms and implementation: safe GC through strong static typing

Simula 67 (if I remember right) did not have automatic garbage collection, and neither had C++ implementations. The official justification in the case of C++ was methodological: C programmers are used to exerting manual control of memory usage. But the real obstacle was technical: compatibility with C makes it impossible to have a good GC. More precisely, to have a sound and complete GC. A GC is sound if it will only reclaim unreachable objects; it is complete if it will reclaim all unreachable objects. With a C-based language supporting casts (e.g. between integers and pointers) and pointer arithmetic, it is impossible to achieve soundness if we aim at a reasonable level of completeness: a pointer can masquerade as an integer, only to be cast back into a pointer later on, but in the meantime the garbage collector, not recognizing it as a pointer, may have wrongly reclaimed the corresponding object. Catastrophe. It is only possible in such a language to have a conservative GC, which will treat as a pointer any integer whose value could possibly be a pointer (because its value lies between the bounds of the program’s data addresses in memory). Then, out of precaution, it will not reclaim the objects at the corresponding address. This approach makes the GC sound but it is only a heuristics, and it may be over-conservative at times, wrongly leaving dead (i.e. unreachable) objects around. The result is, inevitably, a program with memory leaks — usually unacceptable in practice.

Smalltalk, like Lisp, made garbage collection possible, but was not a typed language and missed on the performance benefits of treating simple values like integers as a non-OO language would. Although in this case I do not at the moment have a specific bibliographic reference, I believe that it is in the context of Eiffel that the close connection between strong static typing (avoiding mechanisms such as casts and pointer arithmetic) and the possibility of sound and complete garbage collection was first clearly explained. Explained in particular to some of the future designers of Java, which uses a similar approach, also taken over later on by C#.

By the way, no one will laugh at you today for considering garbage collection as a kind of basic human right for programmers, but for a long time it was quite sulfurous. Here is an extract of the review I got when I submitted the first Eiffel paper to IEEE <em>Transactions on Software Engineering:

Software engineering: primacy of code

Agile methods are widely and properly lauded for emphasizing the central role of code, against designs and other non-executable artifacts. By reading the agile literature you might be forgiven for believing that no one brought up that point before.

Object Success (1995) makes the argument very clearly. For example, chapter 3, page 43:

Code is to our industry what bread is to a baker and books to a writer. But with the waterfall code only appears late in the process; for a manager this is an unacceptable risk factor. Anyone with practical experience in software development knows how many things can go wrong once you get down to code: a brilliant design idea whose implementation turns out to require tens of megabytes of space or minutes of response time; beautiful bubbles and arrows that cannot be implemented; an operating system update, crucial to the project which comes five weeks late; an obscure bug that takes ages to be fixed. Unless you start coding early in the process, you will not be able to control your project.

Such discourse was subversive at the time; the wisdom in software engineering was that you need to specify and design a system to death before you even start coding (otherwise you are just a messy “hacker” in the sense this word had at the time). No one else in respectable software engineering circles was, as far as I know, pushing for putting code at the center, the way the above extract does.

Several years later, agile authors started making similar arguments, but I don’t know why they never referenced this earlier exposition, which still today I find not too bad. (Maybe they decided it was more effective to have a foil, the scorned Waterfall, and to claim that everyone else before was downplaying the importance of code, but that was not in fact everyone.)

Just to be clear, Agile brought many important ideas that my publications did not anticipate; but this particular one I did.

Software engineering: the roles of managers

Extreme Programming and Scrum have brought new light on the role of managers in software development. Their contributions have been important and influential, but here too they were for a significant part prefigured by a long discussion, altogether two chapters, in Object Success (1995).

To realize this, it is enough to read the titles of some of the sections in those chapters, describing roles for managers (some universal, some for a technical manager): “risk manager”, “interface with the rest of the world” (very scrummy!), “protector of the team’s sanity”, “method enforcer” (think Scrum Master), “mentor and critic”. Again, as far as I know, these were original thoughts at the time; the software engineering literature for the most part did not talk about these issues.

Software engineering: outsourcing

As far as I know the 2006 paper Offshore Development: The Unspoken Revolution in Software Engineering was the first to draw attention, in the software engineering community, to the peculiar software engineering challenges of distributed and outsourced development.

Software engineering: automatic testing

The AutoTest project (with many publications, involving I. Ciupa, A. Leitner, Y. Wei, M. Oriol, Y. Pei, M. Nordio and others) was not the first to generate tests automatically by creating numerous instances of objects and calling applicable operations (it was preceded by Korat at MIT), but it was the first one to apply this concept with Design by Contract mechanisms (without which it is of little practical value, since one must still produce test oracles manually) and the first to be integrated in a production environment (EiffelStudio).

Software engineering: make-less system building

One of the very first decisions in the design of Eiffel was to get rid of Make files.

Feldman’s Make had of course been a great innovation. Before Make, programmers had to produce executable systems manually by executing sequences of commands to compile and link the various source components. Make enabled them to instead  to define dependencies between components in a declarative way, resulting in a partial order, and then performed a topological sort to produce the sequence of comments. But preparing the list of dependencies remains a tedious task, particularly error-prone for large systems.

I decided right away in the design of Eiffel that we would never force programmers to write such dependencies: they would be automatically extracted from the code, through an exhaustive analysis of the dependencies between modules. This idea was present from the very the first Eiffel report in 1985 (reference [55] in the publication list): Eiffel programmers never need to write a Make file or equivalent (other than for non-Eiffel code, e.g. C or C++, that they want to integrate); they just click a Compile button and the compiler figures out the steps.

Behind this approach was a detailed theoretical analysis of possible relations between modules in software development (in many programming languages), published as the “Software Knowledge Base” at ICSE in 1985. That analysis was also quite instructive and I would like to return to this work and expand it.

Educational techniques: objects first

Towards an Object-Oriented Curriculum ( TOOLS conference, August 1993, see also the shorter JOOP paper in May of the same year) makes a carefully argued case for what was later called the Objects First approach to teaching programming. I would be interested to know if there are earlier publications advocating starting programming education with an OO language.

The article also advocated for the “inverted curriculum”, a term borrowed from work by Bernie Cohen about teaching electrical engineering. It was the first transposition of this concept to software education. In the article’s approach, students are given program components to use, then little by little discover how they are made. This technique met with some skepticism and resistance since the standard approach was to start from the very basics (write trivial programs), then move up. Today, of course, many introductory programming courses similarly provide students from day one with a full-fledged set of components enabling them to produce significant programs.

More recent articles on similar topics, taking advantage of actual teaching experience, are The Outside-In Method of Teaching Programming (2003) and The Inverted Curriculum in Practice (at ICSE 2006, with Michela Pedroni). The culmination of that experience is the textbook Touch of Class from 2009.

Educational techniques: Distributed Software Projects

I believe our team at ETH Zurich (including among others M. Nordio, J. Tschannen, P. Kolb and C. Estler and in collaboration with C. Ghezzi, E. Di Nitto and G. Tamburrelli at Politecnico di Milano, N. Aguirre at Rio Cuarto and many others in various universities) was the first to devise,  practice and document on a large scale (see publications and other details here) the idea of an educational software project conducted in common by student groups from different universities. It yielded a wealth of information on distributed software development and educational issues.

Educational techniques: Web-based programming exercises

There are today a number of cloud-based environments supporting the teaching of programming by enabling students to compile and test their programs on the Web, benefiting from a prepared environment (so that they don’t have to download any tools or prepare control files) and providing feedback. One of the first — I am not sure about absolute precedence — and still a leading one, used by many universities and applicable to many programming languages, is Codeboard.

The main developer, in my chair at ETH Zurich, was Christian Estler, supported in particular by M. Nordio and M. Piccioni, so I am only claiming a supporting role here.

Educational techniques: key CS/SE concepts

The 2001 paper Software Engineering in the Academy did a good job, I think, of defining the essential concepts to teach in a proper curriculum (part of what Jeannette Wing’s 2006 paper called Computational Thinking).

Program verification: agents (delegates etc.)

Reasoning about Function Objects (ICSE 2010, with M. Nordio, P. Müller and J. Tschannen) introduced verification techniques for objects representing functions (such as agents, delegates etc., see above) in an OO language. Not sure whether there were any such techniques before.

Specification languages: Z

The Z specification language has been widely used for formal development, particularly in the UK. It is the design of J-R Abrial. I may point out that I was a coauthor of the first publication on Z in English (1980),  describing a version that preceded the adaptation to a more graphical-style notation done later at Oxford. The first ever published description of Z, pertaining to an even earlier version, was in French, in my book Méthodes de Programmation (with C. Baudoin), Eyrolles, 1978, running over 15 pages (526-541), with the precise description of a refinement process.

Program verification: exceptions

Largely coming out of the PhD thesis of Martin Nordio, A Sound and Complete Program Logic for Eiffel (TOOLS 2009) introduces rules for dealing with exceptions in a Hoare-style verification framework.

Program verification: full library, and AutoProof

Nadia Polikarpova’s thesis at ETH, aided by the work of Carlo Furia and Julian Tschannen (they were the major contributors and my participation was less important), was as far as I know the first to produce a full functional verification of an actual production-quality reusable library. The library is EiffelBase 2, covering fundamental data structures.

AutoProof — available today, as a still experimental tool, through its Web interface, see here — relied on the AutoProof prover, built by the same team, and itself based on Microsoft Research’s Boogie and Z3 engines.

More

There are more concepts worthy of being included here, but for today I will stop here.

Notes

[A] One point of divergence between usual presentations of the substitution principle and the view in OOSC and my other publications is the covariance versus contravariance of routine argument types. It reflects a difference of views as to what the proper policy (both mathematically sound and practically usable) should be.

[B]  The GoF book does not cite OOSC for the command or bridge patterns. For the command pattern it cites (thanks to Adam Kosmaczewski for digging up the GoF text!) a 1985 SIGGRAPH paper by Henry Lieberman (There’s More to Menu Systems than Meets the Screen). Lieberman’s paper describes the notion of command object and mentions undoing in passing, but does not include the key elements of the command pattern (as explained in full in OOSC1), i.e. an abstract (deferred) command class with deferred procedures called (say) do_it and undo_it, then specific classes for each kind of command, each providing a specific implementation of those procedures, then a history list of commands supporting multiple-level undo and redo as explained in OOSC1. (Reading Lieberman’s paper with a 2021 perspective shows that it came tantalizingly close to the command pattern, but doesn’t get to it. The paper does talk about inheritance between command classes, but only to “define new commands as extensions to old commands”, not in the sense of a general template that can be implemented in many specific ways. And it does mention a list of objects kept around to enable recovery from accidental deletions, and states that the application can control its length, as is the case with a history list; but the objects in the list are not command objects, they are graphical and other objects that have been deleted.)

[C] Additional note on the command pattern: I vaguely remember seeing something similar to the OOSC1 technique in an article from a supplementary volume of the OOPSLA proceedings in the late eighties or early nineties, i.e. at the same time or slightly later, possibly from authors from Xerox PARC, but I have lost the reference.

[D] Correction: I just checked the source and learned that the actual Schopenhauer quote (as opposed to the one that is usually quoted) is different; it does not include the part about laughing. So much for my attempts at understanding philosophy.

 

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Time to resurrect PSP?

Let us assume for the sake of the argument that software quality matters. There are many ingredients to software quality, of which one must be the care that every programmer devotes to the job. The Personal Software Process, developed by Watts Humphrey in the 1990s [1], prescribes a discipline that software developers should apply to produce good software and improve their professional ability over their careers. It has enjoyed moderate success but was never a mass movement and rarely gets mentioned nowadays; few software developers, in my experience, even know the name. Those who do often think of it as passé, a touching memory from the era of Monica Lewinsky and the Roseanne show.

Once cleaned of a few obsolete elements, PSP deserves to be known and applied.

PSP came out of Watts Humphrey’s earlier work on the Capability Maturity Model (see my earlier article on this blog, What is wrong with CMMI), a collection of recommended practices and assessment criteria for software processes, originally developed in the mid-eighties for the U.S. military contractor community but soon thereafter embraced by software outsourcing companies (initially, Indian ones) and later by other industries. Responding to complaints that CMM/CMMI, focused on processes in large companies, ignored the needs of smaller ones, and lacked individual guidance for developers, Humphrey developed TSP, the Team Software Process, and PSP.

The most visible part of PSP is a six-step process pictured in the middle of this diagram:
cmmi

The most visible and also the most corny. Who today wants to promise always to follow such a strict sequence of steps? Always to write the code for a module in full before compiling it? (Notice there is no backward arrow, the process is sequential.) Always to test at the end only? Come on. This is the third decade of the 21st century.

Today we compile as we code, using the development environment (IDE) as a brilliant tool to check everything we do or plan to do. For my part, whenever I am writing code and have not compiled my current draft for more than a few minutes I start feeling like an addict in need of a fix; my fix is the Compile button of EiffelStudio. At some eventual stage the compiler becomes a tool to generate excutable code, but long before that it has been my friend, coach, mentor, and doppelgänger, helping me get things (types, null references, inheritance…) right and gently chiding me when I wander off the rails.

As to tests, even if you do not buy into the full dogma of Test-Driven Development (I don’t), they get written and exercised right from the start, as you are writing the code, not afterwards. Compile all the time, test all the time.

It’s not just that a process such as the above ignores the contributions of agile methods, which are largely posterior to PSP. As analyzed in [2], agile is a curious mix of good ideas and a few horrendous ones. But among its durable contributions is the realization that development must be incremental, not a strict succession of separate activities.

This old-style flavor or PSP is probably the reason why it has fallen out of favor. But (like the agile rejection of upfront lifecycle activities) such a reaction is a case of criticism gone too far, ignoring the truly beneficial contributions. Ignore PSP’s outmoded sequence of activities and you will find that PSP’s core message is as relevant today as it ever was. That message is: we should learn from the practices of traditional engineers and apply a strict professional discipline. For example:

  • Keep a log of all activities. (See “Logs” in the above figure.) Engineers are taught to record everything they do; many programmers don’t bother. This practice, however, is essential to self-improvement.
  • Keep measurements of everything you do. (There are lots of things to measure, from hours spent on every kind of task to bugs found, time to fix them etc.)
  • Estimate and plan your work.
  • Clearly define commitments, and meet them.
  • Resist pressure to make unreasonable commitments (something that agilists approach also emphasize).
  • Understand your current performance.
  • Understand your programming style and how it affects various measures. (As an example, code size, as a function of the number of routines, depends on whether you are more concise or more verbose in style).
  • Continually improve your expertise as a professional.

PSP does not limit itself to such exhortations but gives concrete tools to apply the principles, with a view to: measuring, tracking and analyzing your work; learning from your performance variations; and incorporating the lessons learned into your professional practices. On the topic of measurement, for example, PSP includes precise guidelines on what to measure and how to measure it, and how to rely on proxies for quantities that are hard to assess directly. On this last point, PSP includes PROBE (PROxy-Based Estimating, you cannot have a method coming out of the world of US government organizations without cringeworthy acronyms), a general framework for estimating size and resource parameters from directly measurable proxies.

This is what PSP is about: a discipline of personal productivity and growth, emphasizing personal discipline, tracking and constant improvement. It is not hard to learn; a technical report by Humphrey available online [3] provides a sufficient basis to understand the concepts and start a process of self-improvement.

Watts Humphrey himself, as all who had the privilege to meet him can testify, was a model of seriousness and professionalism, the quintessential engineer. (I also remember him as the author of what may be the best pun I ever heard — ask me sometime.) PSP directly reflects these qualities and — ignoring its visible but in the end unimportant remnants from outdated technical choices — should be part of every software engineering curriculum and every software engineer’s collection of fundamental practices.

References

[1] Watts Humphrey, Introduction to the Personal Software Process, Addison-Wesley, 1996.

[2] Bertrand Meyer: Agile! The Good, the Hype and the Ugly, Springer, 2014, see here.

[3] Watts Humphrey, The Personal Software Process, Software Engineering Institute Technical Report CMU/SEI-2000-TR-022, available (in PDF, free) here.

 

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

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Fan mail

Received this today from a heretofore unknown correspondent (I don’t often check Facebook Messenger but just happened to). Name removed (I am not sure he would want me to identify him), text translated from another language into English.

Hello, thanks for your book “Object-Oriented Software Construction” [read in a translation]. I read it after a horrible failure of a project on which I was a consultant. Another consultant was my technical leader. He was truly insufferable but I appreciated him for one reason: his code! I had never seen such “beautiful” program code; he was using principles of genericity, dynamic binding and others, which were totally unknown to me after the lousy programming education I had received. He had insulted me, telling me that I was no developer at all; I was deeply offended since I could feel that he was right. In spite of his unbearable personality I wanted to learn at his side, but he was far too selfish, seeing me just as a competitor, even if a pathetic one. He had a book on the side of his desk… and it’s that book that enabled me to understand where he had learned all those OO design methods. That book, obviously, was yours, and I acquired a copy for myself. I sincerely think that it should be used as textbook in educational institutions. And I really wanted to thank you for writing it. I hope to become a real developer thanks to you. So, thank you.

Note 1: Thanks to you.

Note 2: There is also the intro programming text, Touch of Class (Amazon page).

Note 3 (to my fan club): You are welcome to take advantage of the ideas and there is actually no compelling requirement to be, in addition, “insufferable”.

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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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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.

Note added 24 December 2020: you will find a longer exposition in a later article on this blog.

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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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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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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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Festina retro

We “core” computer scientists and software engineers always whine that our research themes forever prevent us, to the delight of our physicist colleagues but unjustly, from reaching the gold standard of academic recognition: publishing in Nature. I think I have broken this barrier now by disproving the old, dusty laws of physics! Brace yourself for my momentous discovery: I have evidence of negative speeds.

My experimental setup (as a newly self-anointed natural scientist I am keen to offer the possibility of replication) is the Firefox browser. I was downloading an add-on, with a slow connection, and at some point got this in the project bar:

Negative download speed

Negative speed! Questioning accepted wisdom! Nobel in sight! What next, cold fusion?

I fear I have to temper my enthusiasm in deference to more mundane explanations. There’s the conspiracy explanation: the speed is truly negative (more correctly, it is a “velocity”, a vector of arbitrary direction, hence in dimension 1 possibly negative); Firefox had just reversed the direction of transfer, surreptitiously dumping my disk drive to some spy agency’s server.

OK, that is rather far-fetched. More likely, it is a plain bug. A transfer speed cannot be negative; this property is not just wishful thinking but should be expressed as an integral part of the software. Maybe someone should tell Firefox programmers about class invariants.

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Devops (the concept, and a workshop announcement)

One of the most significant recent developments in software engineering is the concept of Devops*. Dismissing the idea as “just the latest buzzword” would be wrong. It may be a buzzword but it reflects a fundamental change in the way we structure system development; with web applications in particular the traditional distinctions between steps of development, V&V** and deployment fade out. If you are using Microsoft Word, you know or can easily find out the version number; but which version of your search engine are you using?

With the new flexibility indeed come new risks, as when a bug in the latest “devopsed”  version of Google Docs caused me to lose a whole set of complex diagrams irretrievably; an earlier article on this blog (“The Cloud and Its Risks“, October 2010) told the story.

In the new world of continuous integrated development/V&V/deployment, software engineering principles are more necessary than ever, but their application has to undergo a profound adaptation.

With Jean-Michel Bruel (Toulouse), Elisabetta Di Nitto (Milan) and Manuel Mazzara (Innopolis), we are organizing a workshop on the topic, DEVOPS 18, on 5-6 March 2018 near Toulouse. The Call for Papers is available here, with Springer LNCS proceedings. The submission deadline is January 15, but for that date a 2-page extended abstract is sufficient. I hope that the event will help the community get a better grasp of the software engineering techniques and practices applicable to this new world of software development.

Notes

*I know, it’s supposed to be DevOps (I am not a great fan of upper case in the middle of words).
** Validation & Verification.

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Split the Root: a little design pattern

Many programs take “execution arguments” which the program users provide at the start of execution. In EiffelStudio you can enter them under Execution -> Execution parameters.

The program can access them through the Kernel Library class ARGUMENTS. Typically, the root class of the system inherits from ARGUMENTS and its creation procedure will include something like

if argument_count /= N then
……..print (“XX expects exactly N arguments: AA, BB, …%N”)
else
……..u := argument (1) ; v := argument (2) ; …
……..“Proceed with normal execution, using u, v, …”
end

where N is the number of expected arguments, XX is the name of the program, and AA, …. are the roles of arguments. u, v, … are local variables. The criterion for acceptance could be “at least N” instead of exactly N. The features argument_count and arguments come from class ARGUMENTS.

In all but trivial cases this scheme (which was OK years ago, in a less sophisticated state of the language) does not work! The reason is that the error branch will fail to initialize attributes. Typically, the “Proceed with…” part in the other branch is of the form

               attr1 := u
                attr2 := v
                …
                create obj1.make (attr1, …)
                create obj2.make (attr2, …)
                “Work with obj1, obj2, …”

If you try to compile code of this kind, you will get a compilation error:

Compiler error message

Eiffel is void-safe: it guarantees that no execution will ever produce null-pointer dereference (void call). To achieve this guarantee, the compiler must make sure that all attributes are “properly set” to an object reference (non-void) at the end of the creation procedure. But the error branch fails to initialize obj1 etc.

You might think of replacing the explicit test by a precondition to the creation procedure:

               require
                                argument_count = N

but that does not work; the language definition explicit prohibits preconditions in a root creation procedure. The Ecma-ISO standard (the official definition of the language, available here) explains the reason for the corresponding validity rule (VSRP, page 32):

A routine can impose preconditions on its callers if these callers are other routines; but it makes no sense to impose a precondition on the external agent (person, hardware device, other program…) that triggers an entire system execution, since there is no way to ascertain that such an agent, beyond the system’s control, will observe the precondition.

The solution is to separate the processing of arguments from the rest of the program’s work. Add a class CORE which represents the real core of the application and separate it from the root class, say APPLICATION. In APPLICATION, all the creation procedure does is to check the arguments and, if they are fine, pass them on to an instance of the core class:

                note
                                description: “Root class, processes execution arguments and starts execution”
                class APPLICATION create make feature
                                core: CORE
                                                — Application’s core object
                                make
……..……..……..……..……..……..— Check arguments and proceed if they make sense.
                                                do
                                                             if argument_count /= N then
                                                                                print (“XX expects exactly N arguments: AA, BB, …%N”)
                                                                else
                                                                                create core.make (argument (1), argument (2) ; …)
                                                                                                — By construction the arguments are defined!
                                                                                core.live
                                                                                                — Perform actual work
                                                                                               — (`live’ can instead be integrated with `make’ in CORE.)

                                                                end
                                                end
                 end
 
We may call this little design pattern “Split the Root”. Nothing earth-shattering; it is simply a matter of separating concerns (cutting off the Model from the View). It assumes a system that includes text-based output, whereas many applications are graphical. It is still worth documenting, for two reasons.

First, in its own modest way, the pattern is useful for simple programs; beginners, in particular, may not immediately understand why the seemingly natural way of processing and checking arguments gets rejected by the compiler.

The second reason is that Split the Root illustrates the rules that preside over a carefully designed language meant for carefully designed software. At first it may be surprising and even irritating to see code rejected because, in a first attempt, the system’s root procedure has a precondition, and in a second attempt because some attributes are not initialized — in the branch where they do not need to be initialized. But there is a reason for these rules, and once you understand them you end up writing more solid software.

 

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New session of online Agile course starts now

Just about a year ago I posted this announcement about my just released Agile course:

In spite of all the interest in both agile methods and MOOCs (Massive Open Online Courses) there are few courses on agile methods; I know only of some specialized MOOCs focused on a particular language or method.

I produced for EdX, with the help of Marco Piccioni, a new MOOC entitled Agile Software Development. It starts airing today and is supported by exercises and quizzes. The course uses some of the material from my Agile book.

The course is running again! You can find it on EdX here.

Such online courses truly “run”: they are not just canned videos but include exercises and working material on which you can get feedback.

Like the book (“Agile: The Good, the Hype and the Ugly“, Springer), the course is a tutorial on agile methods, presenting an unbiased analysis of their benefits and limits.

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Concurrency/verification positions at Politecnico di Milano

As part of the continuation of the ERC Advanced Investigator Grant project “Concurrency Made Easy” (started at ETH Zurich, see the project pages at cme.ethz.ch), I have positions at Politecnico di Milano for:

  • Postdocs (having a doctoral degree)
  • Research associates (officially: “Assegno di Ricerca”, with the requirement of having a master degree), which can lead to a PhD position.

The deadline for applications is October 11. Please contact me directly if interested. What I expect:

  • The requisite degrees as stated above.
  • Innovative and enterprising spirit, passion for quality work in software engineering.
  • Either or both of excellent programming abilities and strong CS theoretical background.
  • Knowledge of as many of possible of: object-oriented programming, concurrency/parallelism, software verification/formal methods, Eiffel.
  • Familiarity with the basics of the project as described in the project pages at the URL above.
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Feature interactions, continued

Microsoft Office tools offer  features for (1) spelling correction and (2) multi-language support. They are not very good at working together, another example of the perils of feature interaction.

Spelling correction will by default
Image10
when misspelled, but in the case of common misspellings known to the tools it will simply correct words without bothering the user. For example if you type “bagage” it will change it silently to “baggage”. This feature can be turned off, and the list of known misspellings can be edited, but most people use the defaults, as I am assuming here.

Multi-language support enables you to “install” several languages. Then when you type a text it will after a few words guess the relevant language. From then on it can  apply the proper spell checks and corrections.

These features are both useful, and by and large they both work. (The second one is not always reliable. I regularly end up, particularly in Microsoft Word, with entire paragraphs underlined in red because for some inscrutable reason the tool assigns them the wrong language. In such cases you must tell it manually what language it should apply.)

Their combination can lead to funny results. Assume your default language is English but you also have French installed, and you are typing an email in French under Outlook. Your email will say “Le bagage est encore dans l’avion”, meaning “The baggage is still in the plane”. The word “baggage” has one more “g” in English than in French. You start typing  “Le bagage”, but because at that point the tool assumes English it corrects it silently:

Image4

Next you type “est encore”:

Image5

The  word “est” (is) gets flagged because (unlike “encore”, here meaning “still”) it does not exist in English. When you add the next word, “dans” (in), the tool is still assuming an English text, so it flags it too:

Image6

Now you type “l” and when you add the apostrophe, you can almost hear a “silly me, I see now, that’s French!”. Outlook  switches languages and unflags the previously flagged words, removing the red squiggle under the ones that are correct in French:

Image7

But that is too late for “baggage”: the automatic respelling of “bagage”, coming from the default assumption that the text was in English, no longer makes sense now that we know it is in French. So the word gets flagged as a misspelling. You have to go back and correct it yourself. That is frustrating, since you typed the correct spelling in the first place (“bagage”), and it is the tool that messed it up.

This bug hits me often. It is indeed a bug, which can introduce misspellings into a text when the user typed it correctly. When the tool recognizes that the text is in another language than the one assumed so far, and performs a second pass over the part already analyzed, it should reconsider both the words previously flagged as misspellings but also those previously corrected. There is no justification for doing one and not the other.

Among the world’s most momentous problems, this one does not rank very high. It is only a small annoyance, and only a tiny set of people will ever notice it. But it provides another illustration of how tricky it is to go from good individual features to a good overall design.


Related:

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The perils of feature interaction

One of the most delicate aspects of design is feature interaction. As users, we suffer daily from systems offering features that individually make sense but clash with each other. In my agile book [1] I explained in detail, building on the work of Pamela Zave, why this very problem makes one of the key ideas of agile methods,  the reliance on “user stories” for requirements, worthless and damaging.

A small recent incident reminded me of the perils of feature interaction. I used my Lenovo W540 laptop without power for a short while, then reached a sedentary location and plugged it in. Hence my surprise when, some hours later, it started beeping to alert me that it was running out of battery. The natural reactions — check the outlet and the power cord — had no effect. I found the solution, but just in time: otherwise, including if I had not heard the warning sound, I would have been unable to use the laptop any further. That’s right: I would not have been able to restart the computer at all, even with access to a power outlet, and even though it was perfectly functional and so was its (depleted) battery. The reason is that the problem arose from a software setting, which (catch-22 situation) I could not correct without starting the computer [2].

The only solution would have been to find another, non-depleted battery. That is not a trivial matter if you have traveled with your laptop outside of a metropolis: the W540 has a special battery which ordinary computer shops do not carry [3].

The analysis of what made such a situation possible must start with the list of relevant hardware and software product features.

Hardware:

  • HA. This Lenovo W series includes high-end laptops with high power requirements, which the typical 65-watt airplane power jack does not satisfy.
  • HB. With models prior to the W540, if you tried to connect a running laptop to the power supply in an airplane, it would not charge, and the power indicator would start flickering.  But you could still charge it if you switched it off.
  • HC. The W540 effectively requires 135 watts and will not take power from a 65-watt power source under any circumstances.

Software:

  • SA. The operating system (this discussion assumes Windows) directly reflects HC by physically disabling charging if the laptop is in the “Airplane” power mode.
  • SB. If you disable wireless, the operating system automatically goes into the “Airplane” power mode.
  • SC. In the “Airplane” power mode, the laptop, whether or not connected through a charger to a power outlet of any wattage, will not charge. The charging function is just disabled.
  • SD. One can edit power modes to change parameters, such as time to automatic shutoff, but the no-charging property in Airplane mode is not editable and not even mentioned in the corresponding UI dialog. It seems to be a behind-the-scenes property magically attached to the power-mode name “Airplane”.
  • SE. There is a function key for disabling wireless: F8. As a consequence of SB it also has the effect of switching to “Airplane” mode.
  • SF. Next to F8 on the keyboard is F7.
  • SG. F7 serves to display the screen content on another monitor (Windows calls it a “projector”). F7 offers a cyclic set of choices: laptop only, laptop plus monitor etc.
  • SH. In the old days (like five years ago), such function keys setting important operating system parameters on laptops used to be activated only if you held them together with a special key labeled “Fn”. For some reason (maybe the requirement was considered too complicated for ordinary computer users) the default mode on Lenovo laptops does not use the “Fn” key anymore: you just press the desired key, such as F7 or F8.
  • SI. You can revert to the old mode, requiring pressing “Fn”, by going into the BIOS and performing some not-absolutely-trivial steps, making this possibility the preserve of techies. (Helpfully, this earlier style is called “Legacy mode”, as a way to remind you that your are an old-timer, probably barely graduated from MS-DOS and still using obsolete conventions. In reality, the legacy mode is the right one to use, whether for techies or novices: it is all too easy to hit a function key by mistake and get totally unexpected results. The novice, not the techie, is the one who will be completely confused and panicked as a result. The first thing I do with a new laptop is to go to the BIOS and set legacy mode.)

By now you have guessed what happened in my case, especially once you know that I had connected the laptop to a large monitor and had some trouble getting that display to work. In the process I hit Fn-F7 (feature SG) several times.  I must have mistakenly (SF) pressed F8 instead of F7 at some point. Normally, Legacy mode (SI) should have made me immune to the effects of hitting a function key by mistake, but I did use the neighboring key F7 for another purpose. Hitting F8 disabled wireless (SE) and switched on Airplane power mode (SB). At that point the laptop, while plugged in correctly, stopped charging (SC, SD).

How did I find out? Since I was looking for a hardware problem I could have missed the real cause entirely and ended up with a seemingly dead laptop. Fortunately I opened the Power Options dialog to see what it said about the battery. I noticed that among the two listed power plans the active one was not “Power Saver”, to which I am used, but “Airplane”. I did not immediately pay  attention to that setting; since I had not used the laptop for a while I just thought that maybe the last time around I had switched on “Airplane”, even though that made little sense since I was not even aware of the existence of that option. After trying everything else, though, I came back to that intriguing setting, changed to the more usual “Power Saver”, and the computer started to charge again. I was lucky to have a few percent of battery still left at that point.

Afterwards I found a relevant discussion thread on a Lenovo user forum.

As is often the case in such feature-interaction mishaps, most of the features make sense individually [4]. What causes trouble is some unforeseen combination of features.

There is no sure way to avoid such trouble, but there is a sure way to cause it: design a system feature by feature, as with user stories in agile development. The system must do this and it must do that. Oh, by the way, it must also do that. And that. User stories have one advantage: everyone understands them. But that is also their limitation. Good requirements and design require professionals who can see the whole beyond the parts.

A pernicious side of this situation is that many people believe that use cases and user stories are part of object-oriented analysis, whereas the OO approach to requirements and design is the reverse: rise above individual examples to uncover the fundamental abstractions.

As to my laptop, it is doing well, thanks. And I will be careful with function keys.

Reference and notes

[1] Bertrand Meyer: Agile! The Good, the Hype and the Ugly, Springer, 2014,  Amazon page: here, book page: here. A description of the book appeared here on this blog at the time of publication.

[2] Caveat: I have not actually witnessed this state in which a plugged-in laptop will not restart. The reason is simply that I do not have an alternate battery at the moment so I cannot perform the experiment with the almost certain result of losing the use of my laptop. I will confirm the behavior as soon as I have access to a spare battery.

[3] It has been my systematic experience over the past decade and a half that Lenovo seems to make a point, every couple of years, to introduce new models with incompatible batteries and docking stations. (They are also ever more incredibly bulky, with the one for the W540 almost as heavy as the laptop itself. On the other hand the laptops are good, otherwise I would not be bothering with them.)

[4] One exception here is feature SB: switching wireless off does not necessaril y mean you want to select a specific power mode! It is a manifestation of the common syndrome  of software tools that think they are smarter than you, and are not. Another exception is SE: to let a simple key press change fundamental system behavior is to court disaster. But I had protected myself by using legacy mode and was hit anyway.

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AutoProof workshop: Verification As a Matter of Course

The AutoProof technology pursues the goal of “Verification As a Matter Of Course”, integrated into the EVE development environment. (The AutoProof  project page here; see particularly the online interactive tutorial.) A one-day workshop devoted to the existing AutoProof and current development will take place on October 1 near Toulouse in France. It is an informal event (no proceedings planned at this point, although based on the submissions we might decide to produce a volume), on a small scale, designed to bring together people interested in making the idea of practical verification a reality.

The keynote will be given by Rustan Leino from Microsoft Research, the principal author of the Boogie framework on which the current implementation of AutoProof relies.

For submissions (or to attend without submitting) see the workshop page here. You are also welcome to contact me for more information.

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Agile MOOC starts this week

In spite of all the interest in both agile methods and MOOCs (Massive Open Online Courses) there are few courses on agile methods; I know only of some specialized MOOCs focused on a particular language or method.

I produced for EdX, with the help of Marco Piccioni, a new MOOC entitled Agile Software Development. It starts airing today and is supported by exercises and quizzes. The course uses some of the material from my Agile book.

Registration is free and open to anyone at this address.

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Robotics and concurrency

Many robotics applications are by nature concurrent; in his ongoing PhD work, Andrey Rusakov [1] is building a comprehensive concurrent robot programming framework, Roboscoop [2], based on the SCOOP model for simple concurrent object-oriented programming [3] and the Ros operating system. As part of this work it is important to know how much robotics applications use concurrency, stay away from concurrency — perhaps because programmers are afraid of the risks — and could benefit from more concurrency. Rusakov has prepared a questionnaire [4] to help find out. If you have experience in robot programming, please help him by answering the questionnaire, which takes only a few minutes.

References

[1] Rusakov’s home page here.

[2] Roboscoop project page, here,

[3] Simple Concurrent Object-Oriented Programming, see here.

[4] The questionnaire is here.

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Design by Contract: ACM Webinar this Thursday

A third ACM webinar this year (after two on agile methods): I will be providing a general introduction to Design by Contract. The date is this coming Thursday, September 17, and the time is noon New York (18 Paris/Zurich, 17 London, 9 Los Angeles, see here for hours elsewhere). Please tune in! The event is free but requires registration here.

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New paper: Theory of Programs

Programming, wrote Dijkstra many years ago, is a branch of applied mathematics. That is only half of the picture: the other half is engineering, and this dual nature of programming is part of its attraction.

Descriptions of the mathematical side are generally, in my view, too complicated. This article [1] presents a mathematical theory of programs and programming based on concepts taught in high school: elementary set theory. The concepts covered include:

  • Programming.
  • Specification.
  • Refinement.
  • Non-determinism.
  • Feasibility.
  • Correctness.
  • Programming languages.
  • Kinds of programs: imperative, functional, object-oriented.
  • Concurrency (small-step and large-step)
  • Control structures (compound, if-then-else and Dijkstra-style conditional, loop).
  • State, store and environment.
  • Invariants.
  • Notational conventions for building specifications and programs incrementally.
  • Loop invariants and variants.

One of the principal ideas is that a program is simply the description of a mathematical relation. The program text is a rendering of that relation. As a consequence, one may construct programming languages simply as notations to express certain kinds of mathematics. This approach is the reverse of the usual one, where the program text and its programming languages are the starting point and the center of attention: theoreticians develop techniques to relate them to mathematical concepts. It is more effective to start from the mathematics (“unparsing” rather than parsing).

All the results (74 properties expressed formally, a number of others in the text) are derived as theorems from rules of elementary set theory; there are no new axioms whatsoever.

The paper also has a short version [2], omitting proofs and many details.

References

[1] Theory of Programs, available here.
[2] Theory of Programs, short version of [1] (meant for quick understanding of the ideas, not for publication), available here.

 

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Computing: the Art, the Magic, the Science

 

My colleagues and I have just finished recording our new MOOC (online course), an official ETH offering on the EdX platform. The preview is available [1] and the course will run starting in September.

As readers of this blog know, I  have enthusiastically, under the impulsion of Marco Piccioni at ETH, embraced MOOC technology to support and spread our courses. The particular target has been the introduction to programming that I have taught for over a decade at ETH based on the Touch of Class textbook [2]. In February this blog announced [3] the release of our first MOOC, embodying the essentials of our ETH course and making it available not only to ETH students but to the whole world. The course does not just include video lectures: it also supports active student participation through online exercises and programs that can be compiled and tested on the cloud, with no software installation. These advanced features result from our research on support for distributed software development (by Christian Estler and Martin Nordio, with Carlo Furia and others).

This first course was a skunkworks project, which we did entirely on our own without any endorsement from ETH or any of the main MOOC players. We and our students have very much benefited from the consequent flexibility, and the use of homegrown technology relying on the MOODLE framework. We will keep this course for our own students and for any outside participant who prefers a small-scale, “boutique” version. But the EdX brand and EdX’s marketing power will enable us to reach a much broader audience. We want to provide the best introductory computing course on the market and the world needs to know about it. In addition, the full support of media services at ETH  helped us reach a higher standard on the technical side. (For our first course, the home-brewed one, we did not have a studio, so that every time an ambulance drove by — our offices are close to the main Zurich hospital — we had to restart the current take.)

The course’s content is not exactly the same: we have broadened the scope from just programming to computing, although it retains a strong programming component. We introduced additional elements such as an interview with Professor Peter Widmayer of ETH on the basics of computer science theory. For both new material and the topics retained from the first version we have adapted to the accepted MOOC practice of short segments, although we did not always exactly meet the eight-minute upper limit that was suggested to us.

We hope that you, and many newcomers, will like the course and benefit from it.

References

[3] EdX course: Computing: Art, Magic, Science, preview available here.

[2] Bertrand Meyer: Touch of Class: Learning how to Program Well, with Objects and Contracts, Springer Verlag, revised printing, 2013, book page here.

[3] Learning to Program, Online: article from this blog, 3 February 2014, available here.

 

 

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