Bertrand Meyer's technology+ blog

I have mentioned this paper before (see the earlier blog entry here) but it is now going to be published [1] and has been significantly revised, both to take referee comments into account and because we found better ways to present the concepts.

We have  endeavored to explain better than in the draft why the concept of negative variable is necessary and why the usual techniques for modeling object-oriented programs do not work properly for the fundamental OO operation, qualified call x.r (…). These techniques are based on substitution and are simply unable to express certain properties (let alone verify them). The affected properties are those involving properties of the calling context or the global project structure.

The basic idea (repeated in part from the earlier post) is as follows. In modeling OO programs, we have to take into account the unique “general relativity” property of OO programming: all the operations you write are expressed relative to a “current object” which changes repeatedly during execution. More precisely at the start of a call x.r (…) and for the duration of that call the current object changes to whatever x denotes — but to determine that object we must again interpret x in the context of the previous current object. This raises a challenge for reasoning about programs; for example in a routine the notation f.some_reference, if f is a formal argument, refers to objects in the context of the calling object, and we cannot apply standard rules of substitution as in the non-OO style of handling calls.

We introduced a notion of negative variable to deal with this issue. During the execution of a call x.r (…) the negation of x , written x’, represents a back pointer to the calling object; negative variables are characterized by axiomatic properties such as x.x’= Current and x’.(old x)= Current.

The paper explains why this concept is necessary, describes the associated formal rules, and presents applications.

Reference

[1] Bertrand Meyer and Alexander Kogtenkov: Negative Variables and the Essence of Object-Oriented Programming, to appear in Specification, Algebra, and Software, eds. Shusaku Iida, Jose Meseguer and Kazuhiro Ogata, Springer Lecture Notes in Computer Science, 2014, to appear. See text here.

In honor of Niklaus Wirth’s 80-th birthday we are organizing a symposium at ETH on February 20, 2014. This is a full-day event with invited talks by:

• Vint Cerf
• Hans Eberlé
• Michael Franz
• me
• Carroll Morgan
• Martin Odersky
• Clemens Szyperski
• Niklaus Wirth himself

From the symposium’s web page:

Niklaus Wirth was a Professor of Computer Science at ETH Zürich, Switzerland, from 1968 to 1999. His principal areas of contribution were programming languages and methodology, software engineering, and design of personal workstations. He designed the programming languages Algol W, Pascal, Modula-2, and Oberon, was involved in the methodologies of structured programming and stepwise refinement, and designed and built the workstations Lilith and Ceres. He published several text books for courses on programming, algorithms and data structures, and logical design of digital circuits. He has received various prizes and honorary doctorates, including the Turing Award, the IEEE Computer Pioneer, and the Award for outstanding contributions to Computer Science Education.

Participation is free (including breaks, lunch and the concluding “Apéro”) but space is strictly limited and we expect to run out of seats quickly. So if you are interested (but only if you are certain to attend) please register right away.

Symposium page and access to registration form: here.

The 2013 Informatics Europe “Best Practices in Education” award was devoted, this year, to initiatives for teaching informatics in schools [1].  It was given out last week at the European Computer Science Summit in Amsterdam [2]. Two teams shared it, one from Poland and the other from Romania. Both teams showed excellent projects, but the second was beyond anything I expected.

The project comes from the Hungarian-speaking Sapientia University in Transylvania and is devoted to teaching algorithms visually and “at the same time enhancing intercultural communication” in the region. It illustrates the classical sorting algorithms through folk dances. Quicksort is Hungarian, selection sort is gypsy, merge sort is “Transylvanian-saxon”. I think my favorite is Shell sort [3]. For more, see their YouTube channel [4].

Now  if only they could act the loop invariants [5].

References

[1] 2013 Best Practices in Education award, see here.

[2] 2013 European Computer Science Summit, here.

[3] “Shell sort with Hungarian (Székely) folk dance”, see here.

[4] YouTube Algorythmics channel, here.

[5] Carlo Furia, Bertrand Meyer and Sergey Velder: Loop invariants: Analysis, Classification and Examples, in ACM Computing Surveys, Septembre 2014, to appear, available here.

Recently I mentioned the first law of branching (see earlier article) to Walter Tichy, famed creator of RCS, the system that established modern configuration management. He replied with the following anecdote, which is worth reproducing in its entirety (in his own words):

I started work on RCS in 1980, because I needed an alternative for SCCS, for which the license cost would have been prohibitive. Also, I wanted to experiment with reverse deltas. With reverse deltas, checking out the latest version is fast, because it is stored intact. For older ones, RCS applied backward deltas. So the older revisions took longer to extract, but that was OK, because most accesses are to the newest revision anyway.

At first, I didn’t know how to handle branches in this scheme. Storing each branch tip in full seemed like a waste. So I simply left out the branches.

It didn’t take long an people were using RCS. Bill Joy, who was at Berkeley at the time and working on Berkeley Unix, got interested. He gave me several hints about unpleasant features of SCCS that I should correct. For instance, SCCS didn’t handle identification keywords properly under certain circumstances, the locking scheme was awkward, and the commands too. I figured out a way to solve these issue. Bill was actually my toughest critic! When I was done with all the modifications, Bill cam back and said that he was not going to use RCS unless I put in branches. So I figured out a way. In order to reconstruct a branch tip, you start with the latest version on the main trunk, apply backwards deltas up to the branch point, and then apply forward deltas out to the branch tip. I also implemented a numbering scheme for branches that is extensible.

When discussing the solution, Bill asked me whether this scheme meant that it would take longer to check in and out on branches. I had to admit that this was true. With the machines at that time (VAXen) efficiency was important. He thought about this for a moment and then said that that was actually great. It would discourage programmers from using branches! He felt they were a necessary evil.

A new version of my Touch of Class [1] programming textbook is available. It is not quite a new edition but more than just a new printing. All the typos that had been reported as of a few months ago have been corrected.

The format is also significantly smaller. This change is more than a trifle. When а  reader told me for the first time “really nice book, pity it is so heavy!”, I commiserated and did not pay much attention. After twenty people said that, and many more after them, including professors looking for textbooks for their introductory programming classes, I realized it was a big deal. The reason the book was big and heavy was not so much the size of the contents (876 is not small, but not outrageous for a textbook introducing all the fundamental concepts of programming). Rather, it is a technical matter: the text is printed in color, and Springer really wanted to do a good job, choosing thick enough paper that the colors would not seep though. In addition I chose a big font to make the text readable, resulting in a large format. In fact I overdid it; the font is bigger than necessary, even for readers who do not all have the good near-reading sight of a typical 19-year-old student.

We kept the color and the good paper,  but reduced the font size and hence the length and width. The result is still very readable, and much more portable. I am happy to make my contribution to reducing energy consumption (at ETH alone, think of the burden on Switzerland’s global energy bid of 200+ students carrying the book — as I hope they do — every morning on the buses, trains and trams crisscrossing the city!).

Springer also provides electronic access.

Touch of Class is the textbook developed on the basis of the Introduction to Programming course [2], which I have taught at ETH Zurich for the last ten years. It provides a broad overview of programming, starting at an elementary level (zeros and ones!) and encompassing topics not usually covered in introductory courses, even a short introduction to lambda calculus. You can get an idea of the style of coverage of such topics by looking up the sample chapter on recursion at touch.ethz.ch. Examples of other topics covered include a general introduction to software engineering and software tools. The presentation uses full-fledged object-oriented concepts (including inheritance, polymorphism, genericity) right from the start, and Design by Contract throughout. Based on the “inverted curriculum” ideas on which I published a number of articles, it presents students with a library of reusable components, the Traffic library for graphical modeling of traffic in a city, and builds on this infrastructure both to teach students abstraction (reusing code through interfaces including contracts) and to provide them models of high-quality code for imitation and inspiration.

For more details see the article on this blog that introduced the book when it was first published [3].

References

[1] Bertrand Meyer, Touch of Class: An Introduction to Programming Well Using Objects and Contracts, Springer Verlag, 2nd printing, 2013. The Amazon page is here. See the book’s own page (with slides and other teaching materials, sample chapter etc.) here. (Also available in Russian, see here.)

[2] Einführung in die Programmierung (Introduction to Programming) course, English course page here.

[3] Touch of Class published, article on this blog, 11 August 2009, see [1] here.

I have mentioned this paper before but as a draft. It has now been accepted by ACM’s Computing Surveys and is scheduled to appear in September 2014; the current text, revised from the previous version, is available [1].

Here is the abstract:

Software verification has emerged as a key concern for ensuring the continued progress of information technology. Full verification generally requires, as a crucial step, equipping each loop with a “loop invariant”. Beyond their role in verification, loop invariants help program understanding by providing fundamental insights into the nature of algorithms. In practice, finding sound and useful invariants remains a challenge. Fortunately, many invariants seem intuitively to exhibit a common flavor. Understanding these fundamental invariant patterns could therefore provide help for understanding and verifying a large variety of programs.

We performed a systematic identification, validation, and classification of loop invariants over a range of fundamental algorithms from diverse areas of computer science. This article analyzes the patterns, as uncovered in this study,governing how invariants are derived from postconditions;it proposes a taxonomy of invariants according to these patterns, and presents its application to the algorithms reviewed. The discussion also shows the need for high-level specifications based on “domain theory”. It describes how the invariants and the corresponding algorithms have been mechanically verified using an automated program prover; the proof source files are available. The contributions also include suggestions for invariant inference and for model-based specification.

Reference

[1] Carlo Furia, Bertrand Meyer and Sergey Velder: Loop invariants: Analysis, Classification and Examples, in ACM Computing Surveys, to appear in September 2014, preliminary text available here.

This report continues the series of ICSE 2013 article previews (see the posts of these last few days, other than the DOSE announcement), but is different from its predecessors since it talks about a paper from our group at ETH, so you should not expect any dangerously delusional,  disingenuously dubious or downright deceptive declaration or display of dispassionate, disinterested, disengaged describer’s detachment.

The paper [1] (mentioned on this blog some time ago) is entitled How good are software specifications? and will be presented on Wednesday by Nadia Polikarpova. The basic result: stronger specifications, which capture a more complete part of program functionality, cause only a modest increase in specification effort, but the benefits are huge; in particular, automatic testing finds twice as many faults (“bugs” as recently reviewed papers call them).

Strong specifications are specifications that go beyond simple contracts. A straightforward example is a specification of a push operation for stacks; in EiffelBase, the basic Eiffel data structure library, the contract’s postcondition will read

item = x                                          /A/
count = old count + 1

where x is the element being pushed, item the top of the stack and count the number of elements. It is of course sound, since it states that the element just pushed is now the new top of the stack, and that there is one more element; but it is also  incomplete since it says nothing about the other elements remaining as they were; an implementation could satisfy the contract and mess up with these elements. Using “complete” or “strong” preconditions, we associate with the underlying domain a theory [2], or “model”, represented by a specification-only feature in the class, model, denoting a sequence of elements; then it suffices (with the convention that the top is the first element of the model sequence, and that “+” denotes concatenation of sequences) to use the postcondition

model = <x> + old model         /B/

which says all there is to say and implies the original postconditions /A/.

Clearly, the strong contracts, in the  /B/ style, are more expressive [3, 4], but they also require more specification effort. Are they worth the trouble?

The paper explores this question empirically, and the answer, at least according to the criteria used in the study, is yes.  The work takes advantage of AutoTest [5], an automatic testing framework which relies on the contracts already present in the software to serve as test oracles, and generates test cases automatically. AutoTest was applied to both to the classic EiffelBase, with classic partial contracts in the /A/ style, and to the more recent EiffelBase+ library, with strong contracts in the /B/ style. AutoTest is for Eiffel programs; to check for any language-specificity in the results the work also included testing a smaller set of classes from a C# library, DSA, for which a student developed a version (DSA+) equipped with strong model-based contracts. In that case the testing tool was Microsoft Research’s Pex [7]. The results are similar for both languages: citing from the paper, “the fault rates are comparable in the C# experiments, respectively 6 . 10^-3 and 3 . 10^-3 . The fault complexity is also qualitatively similar.”

The verdict on the effect of strong specifications as captured by automated testing is clear: the same automatic testing tools applied to the versions with strong contracts yield twice as many real faults. The term “real fault” comes from excluding spurious cases, such as specification faults (wrong specification, right implementation), which are a phenomenon worth studying but should not count as a benefit of the strong specification approach. The paper contains a detailed analysis of the various kinds of faults and the corresponding empirically determined measures. This particular analysis is for the Eiffel code, since in the C#/Pex case “it was not possible to get an evaluation of the faults by the original developers“.

In our experience the strong specifications are not that much harder to write. The paper contains a precise measure: about five person-weeks to create EiffelBase+, yielding an “overall benefit/effort ratio of about four defects detected per person-day“. Such a benefit more than justifies the effort. More study of that effort is needed, however, because the “person” in the person-weeks was not just an ordinary programmer. True, Eiffel experience has shown that most programmers quickly get the notion of contract and start applying it; as the saying goes in the community, “if you can write an if-then-else, you can write a contract”. But we do not yet have significant evidence of whether that observation extends to model-based contracts.

Model-based contracts (I prefer to call them “theory-based” because “model” means so many other things, but I do not think I will win that particular battle) are, in my opinion, a required component of the march towards program verification. They are the right compromise between simple contracts, which have proved to be attractive to many practicing programmers but suffer from incompleteness, and full formal specification à la Z, which say everything but require too much machinery. They are not an all-or-nothing specification technique but a progressive one: programmers can start with simple contracts, then extend and refine them as desired to yield exactly the right amount of precision and completeness appropriate in any particular context. The article shows that the benefits are well worth the incremental effort.

According to the ICSE program the talk will be presented in the formal specification session, Wednesday, May 22, 13:30-15:30, Grand Ballroom C.

References

[1] Nadia Polikarpova, Carlo A. Furia, Yu Pei, Yi Wei and Bertrand Meyer: What Good Are Strong Specifications?, to appear in ICSE 2013 (Proceedings of 35th International Conference on Software Engineering), San Francisco, May 2013, draft available here.

[2] Bertrand Meyer: Domain Theory: the forgotten step in program verification, article on this blog, see here.

[3] Bernd Schoeller, Tobias Widmer and Bertrand Meyer: Making Specifications Complete Through Models, in Architecting Systems with Trustworthy Components, eds. Ralf Reussner, Judith Stafford and Clemens Szyperski, Lecture Notes in Computer Science, Springer-Verlag, 2006, available here.

[4] Nadia Polikarpova, Carlo Furia and Bertrand Meyer: Specifying Reusable Components, in Verified Software: Theories, Tools, Experiments (VSTTE ‘ 10), Edinburgh, UK, 16-19 August 2010, Lecture Notes in Computer Science, Springer Verlag, 2010, available here.

[5] Bertrand Meyer, Ilinca Ciupa, Andreas Leitner, Arno Fiva, Yi Wei and Emmanuel Stapf: Programs that Test Themselves, IEEE Computer, vol. 42, no. 9, pages 46-55, September 2009, also available here.

[6] Bertrand Meyer, Ilinca Ciupa, Andreas Leitner, Arno Fiva, Yi Wei and Emmanuel Stapf: Programs that Test Themselves, in IEEE Computer, vol. 42, no. 9, pages 46-55, September 2009, also available here.

[7] Nikolai Tillman and Peli de Halleux, Pex: White-Box Generation for .NET, in Tests And Proofs (TAP 2008), pp. 134-153.

(Please note the general disclaimer [1].)

How Misclassification Impacts Bug Prediction [2], an article to be presented on Thursday at ICSE, is the archetype of today’s successful empirical software engineering research, deriving significant results from the mining of publicly available software project repositories — in this case Tomcat5 and three others from Apache, as well as Rhino from Mozilla. The results are in some sense meta-results, because many studies have already mined the bug records of such repositories to draw general lessons about bugs in software development; what Herzig, Just and Zeller now tell us is that the mined data is highly questionable: many problems classified as bugs are not bugs.

The most striking results (announced in a style a bit stentorian to my taste, but indeed striking) are that: every third bug report does not describe a bug, but a request for a new feature, an improvement, better documentation or tests, code cleanup or refactoring; and that out of five program files marked as defective, two do not in fact contain any bug.

These are both false positive results. The repositories signal very few misclassifications the other way: only a small subset of enhancement and improvement requests (around 5%) should have been classified as bugs, and even fewer faulty files are missed (8%, but in fact less than 1% if one excludes an outlier, tomcat5 with 38%, a discrepancy that the paper does not discuss).

The authors have a field day, in the light of this analysis, of questioning the validity of the many studies in recent years — including some, courageously cited, by Zeller himself and coauthors — that start from bug repositories to derive general lessons about bugs and their properties.

The methodology is interesting if a bit scary. The authors (actually, just the two non-tenured authors, probably just a coincidence) analyzed 7401 issue reports manually; more precisely, one of them analyzed all of them and the second one took a second look at the reports that came out from the first step as misclassified, without knowing what the proposed reclassification was, then the results were merged. At 4 minutes per report this truly stakhanovite effort took 90 working days. I sympathize, but I wonder what the rules are in Saarland for experiments involving living beings, particularly graduate students.

Precise criteria were used for the reclassification; for example a report describes a bug, in the authors’ view, if it mentions a null pointer exception (I will skip the opportunity of a pitch for Eiffel’s void safety mechanism), says that the code has to be corrected to fix the semantics, or if there is a “memory issue” or infinite loop. These criteria are reasonable if a bit puzzling (why null pointer exceptions and not other crashes such as arithmetic overflows?); but more worryingly there is no justification for them. I wonder  how much of the huge discrepancy found by the authors — a third or reported bugs are not bugs, and 40% of supposedly defective program files are not defective — can be simply explained by different classification criteria applied by the software projects under examination. The authors give no indication that they interacted with the people in charge of these projects. To me this is the major question hovering over this paper and its spectacular results. If you are in the room and get the chance, don’t hesitate to ask this question on my behalf or yours!

Another obvious question is how much the results depend on the five projects selected. If there ever was room for replicating a study (a practice whose rarity in software engineering we lament, but whose growth prospects are limited by the near-impossibility of convincing selective software engineering venues to publish confirmatory empirical studies), this would be it. In particular it would be good to see some of the results for commercial products.

The article offers an explanation for the phenomena it uncovered: in its view, the reason why so many bug reports end up misclassified is the difference of perspective between users of the software, who complain about the problems they encounter,  and the software professionals  who prepare the actual bug reports. The explanation is plausible but I was surprised not to see any concrete evidence that supports it. It is also surprising that the referees did not ask the authors to provide more solid arguments to buttress that explanation. Yet another opportunity to raise your hand and ask a question.

This (impressive) paper will call everyone’s attention to the critical problem of data quality in empirical studies. It is very professionally prepared, and could, in addition to its specific contributions, serve as a guide on how to get an empirical software engineering paper accepted at ICSE: take a critical look at an important research area; study it from a viewpoint that has not been considered much so far; perform an extensive study, with reasonable methodological assumptions; derive a couple of striking results, making sure they are both visibly stated and backed by the evidence; and include exactly one boxplot.

Notes and references

[1] This article review is part of the “Reading Notes” series. General disclaimer here.

[2] Kim Herzig, Sascha Just and Andreas Zeller: It’s not a Bug, it’s a Feature: How Misclassification Impacts Bug Prediction, in ICSE 2013, available here. According to the ICSE program the paper will be presented on May 23 in the Bug Prediction session, 16 to 17:30.

The following presentations from our ETH group in the ICSE week (International Conference on Software Engineering, San Francisco) address important issues of software specification and verification, describing new techniques that we have recently developed as part of our work building EVE, the Eiffel Verification Environment. One is at ICSE proper and the other at VSTTE (Verified Software: Tools, Theories, Experiments). If you are around please attend them.

Julian Tschannen will present Program Checking With Less Hassle, written with Carlo A. Furia, Martin Nordio and me, at VSTTE on May 17 in the 15:30-16:30 session (see here in the VSTTE program. The draft is available here. I will write a blog article about this work in the coming days.

Nadia Polikarpova will present What Good Are Strong Specifications?, written with , Carlo A. Furia, Yu Pei, Yi Wei and me at ICSE on May 22 in the 13:30-15:30 session (see here in the ICSE program). The draft is available here. I wrote about this paper in an earlier post: see here. It describes the systematic application of theory-based modeling to the full specification and verification of advanced software.

The C2Eif translator developed by Marco Trudel takes C code and translates it into Eiffel; it produces not just a literal translation but a re-engineering version exhibiting object-oriented properties. Trudel defended his PhD thesis last Friday at ETH (the examiners were Hausi Muller from Victoria University, Manuel Oriol from ABB, Richard Paige from the University of York,  and me as the advisor). The thesis is not yet available online but earlier papers describing C2Eif are, all reachable from the project’s home page [1].

At issue is what we do with legacy code. “J’ai plus de souvenirs que si j’avais mille ans”, wrote Charles Baudelaire in Les Fleurs du Mal (“Spleen de Paris”). The software industry is not a thousand years old, but has accumulated even more “souvenirs” than

A heavy chest of drawers cluttered with balance-sheets,
Poems, love letters, lawsuits, romances
And heavy locks of hair wrapped in invoices.

We are suffocating under layers of legacy code heaped up by previous generations of programmers using languages that no longer meet our scientific and engineering standards. We cannot get rid of this heritage; how do we bring it to the modern world? We need automatic tools to wrap it in contemporary code, or, better, translate it into contemporary code. The thesis and the system offer a way out through translation to a modern object-oriented language. It took courage to choose such a topic, since there have been many attempts in the past, leading to conventional wisdom consisting of two strongly established opinions:

• Plain translation: it has been tried, and it works. Not interesting for a thesis.
• Object-oriented reengineering: it has been tried, and it does not work. Not realistic for a thesis.

Both are wrong. For translation, many of the proposed solutions “almost work”: they are good enough to translate simple programs, or even some large programs but on the condition that the code avoids murky areas of C programming such as signals, exceptions (setjmp/longjmp) and library mechanisms. In practice, however, most useful C programs need these facilities, so any tool that ignores them is bound to be of conceptual value only. The basis for Trudel’s work has been to tackle C to OO translation “beyond the easy stuff” (as stated in the title of one of the published papers). This effort has been largely successful, as demonstrated by the translation of close to a million lines of actual C code, including some well-known and representative tools such as the Vim editor.

As to OO reengineering, C2Eif makes a serious effort to derive code that exhibits a true object-oriented design and hence resembles, in its structure at least, what a programmer in the target language might produce. The key is to identify the right data abstractions, yielding classes, and specialization properties, yielding inheritance. In this area too, many people have tried to come up with solutions, with little success. Trudel has had the good sense of avoiding grandiose goals and sticking to a number of heuristics that work, such as looking at the signatures of a set of functions to see if they all involve a common argument type. Clearly there is more to be done in this direction but the result is already significant.

Since Eiffel has a sophisticated C interface it is also possible to wrap existing code; some tools are available for that purpose, such as Andreas Leitner’s EWG (Eiffel Wrapper Generator). Wrapping and translating each have their advantages and limitations; wrapping may be more appropriate for C libraries that someone else is still actively updating  (so that you do not have to redo a translation with every new release), and translation for legacy code that you want to take over and bring up to par with the rest of your software. C2Eif is engineered to support both. More generally, this is a practitioner’s tool, devoting considerable attention to the many details that make all the difference between a nice idea and a tool that really works. The emphasis is on full automation, although more parametrization has been added in recent months.

C2Eif will make a big mark on the Eiffel developer community. Try it yourself — and don’t be shy about telling its author about the future directions in which you think the tool should evolve.

Reference

[1] C2Eif project page, here.

The 2013 LASER summer school, organized by our chair at ETH, will take place September 8-14, once more in the idyllic setting of the Hotel del Golfo in Procchio, on the island of Elba in Italy. This is already the 10th conference; the roster of speakers so far reads like a who’s who of software engineering.

The theme this year is Software for the Cloud and Big Data and the speakers are Roger Barga from Microsoft, Karin Breitman from EMC,  Sebastian Burckhardt  from Microsoft,  Adrian Cockcroft from Netflix,  Carlo Ghezzi from Politecnico di Milano,  Anthony Joseph from Berkeley,  Pere Mato Vila from CERN and I.

LASER always has a strong practical bent, but this year it is particularly pronounced as you can see from the list of speakers and their affiliations. The topic is particularly timely: exploring the software aspects of game-changing developments currently redefining the IT scene.

The LASER formula is by now well-tuned: lectures over seven days (Sunday to Saturday), about five hours in the morning and three in the early evening, by world-class speakers; free time in the afternoon to enjoy the magnificent surroundings; 5-star accommodation and food in the best hotel of Elba, made affordable as we come towards the end of the season (and are valued long-term customers). The group picture below is from last year’s school.

Participants are from both industry and academia and have ample opportunities for interaction with the speakers, who typically attend each others’ lectures and engage in in-depth discussions. There is also time for some participant presentations; a free afternoon to discover Elba and brush up on your Napoleonic knowledge; and a boat trip on the final day.

Information about the 2013 school can be found here.

Lack of a precise context can render discussions of software engineering and particularly of software quality meaningless. Take for example the (usually absurd) statement “We cannot expect that programmers will equip their programs with contracts”. Whom do you mean? A physicist who writes 50 lines of Matlab code to produce a graph illustrating his latest experiment? A member of the maintenance team for Microsoft Word? A programmer on the team for a flight control system? These are completely different constituencies, and the answer is also different. In the last case, the answer is probably that we do not care what the programmers like and do not like. When you buy an electrical device that malfunctions, would you accept from the manufacturer the excuse that differential equations are, really, you see, too hard for our electrical engineers?

In discussing the evolution of software methods and tools we must first specify what and whom we are talking about. The following ABC characterization is sufficient for most cases.

C is for Casual. Programs in that category do all kinds of useful things, and like anything else they should work properly, but if they are not ideal in software engineering terms of reliability, reusability, extendibility and so on — if sometimes they crash, sometimes produce not-quite-right results,  cannot be easily understood or maintained by anyone other than their original developers, target just one platform, run too slowly, eat up too much memory, are not easy to change, include duplicated code — it is not the end of the world. I do not have any scientific figures, but I suspect that most of the world’s software is actually in that category, from JavaScript or Python code that runs web sites to spreadsheet macros. Obviously it has to be good enough to serve its needs, but “good enough” is good enough.

B is for Business. Programs in that category run key processes in the organization. While often far from impeccable, they must satisfy strict quality constraints; if they do not, the organization will suffer significantly.

A is for Acute. This is life-critical software: if it does not work — more precisely, if it does not work exactly right — someone will get killed, someone will lose huge amounts of money, or something else will go terribly wrong. We are talking transportation systems, software embedded in critical devices, make-or-break processes of an organization.

Even in a professional setting, and even within a single company, the three categories usually coexist. Take for example a large engineering or scientific organization.  Some programs are developed to support experiments or provide an answer to a specific technical question. Some programs run the organization, both on the information systems side (enterprise management) and on the technical side (large scientific simulations, experiment set-up). And some programs play a critical role in making strategy decisions, or run the organization’s products.

The ABC classification is independent of the traditional division between enterprise and technical computing. Organizations often handle these two categories separately, whereas in fact they raise issues of similar difficulty and are subject to solutions of a similar nature. It is more important to assess the criticality of each software projects, along the ABC scale.

It is surprising that few organizations make that scale explicit.  It is partly a consequence of that neglect that many software quality initiatives and company-wide software engineering policies are ineffective: they lump everything together, and since they tend to be driven by A-grade applications, for which the risk of bad quality is highest, they create a burden that can be too high for C- and even B-grade developments. People resent the constraints where they are not justified, and as a consequence ignore them where they would be critical. Whether your goal for the most demanding projects is to achieve CMMI qualification or to establish an effective agile process, you cannot impose the same rules on everyone. Sometimes the stakes are high; and sometimes a program is just a program.

The first step in establishing a successful software policy is to separate levels of criticality, and require every development to position itself along the resulting scale. The same observation qualifies just about any discussion of software methodology. Acute, Business or Casual: you must know your ABC.

(This article first appeared in the Communications of the ACM blog.)

The very concept of publication has changed, half of its traditional meaning having disappeared in hardly more than a decade. Or to put it differently (if you will accept the metaphor, explained below), how it has lost its duality: no longer particle, just wave.

Process and product

Some words ending with ation (atio in Latin) describe a change of state: restoration, dilatation. Others describe the state itself, or one of its artifacts: domination, fascination. And yet others play both roles: decoration can denote either the process of embellishing (she works in interior decoration), or an element of the resulting embellishment (Christmas tree decoration).

Since at least Gutenberg, publication has belonged to that last category: both process and artifact. A publication is an artifact, such as an article or a book accessible to a community of readers. We are referring to that view when we say “she has a long publication list” or “Communications of the ACM is a prestigious publication”. But the word also denotes a process, built from the verb “publish” the same way “restoration” is built from “restore” and “insemination” from “inseminate”: the publication of her latest book took six months.

The thesis of this article is that the second view of publication will soon be gone, and its purpose is to discuss the consequences for scientists.

Let me restrict the scope: I am only discussing scientific publication, and more specifically the scientific article. The situation for books is less clear; for all the attraction of the Kindle and other tablets, the traditional paper book still has many advantages and it would be risky to talk about its demise. For the standard scholarly article, however, electronic media and the web are quickly destroying the traditional setup.

That was then . . .

Let us step back a bit to what publication, the process, was a couple of decades ago. When you wrote something, you could send it by post to your friends (Edsger Dijkstra famously turned this idea into his modus operandi, regularly xeroxing his “EWD” memos [1] to a few dozen people) , but if you wanted to make it known to the world you had to go through the intermediation of a PUBLISHER — the mere word was enough to overwhelm you with awe. That publisher, either a non-profit organization or a commercial house, was in charge not only of selecting papers for a conference or journal but of bringing the accepted ones to light. Once you got the paper accepted began a long and tedious process of preparing the text to the publisher’s specifications and correcting successive versions of “galley proofs.” That step could be painful for papers having to do with programming, since in the early days typesetters had no idea how to lay out code. A few months or a couple of years later, you received a package in the mail and proudly opened the journal or proceedings at the page where YOUR article appeared. You would also, usually for a fee, receive fifty or so separately printed (tirés à part) reprints of just your article, typeset the same way but more modestly bound. Ah, the discrete charm of 20-th century publication!

. . . and this is now

Cut to today. Publishers stopped long ago to do the typesetting for you. They impose the format, obligingly give you LaTex, Word or FrameMaker templates, and you take care of everything. We have moved to WYSIWYG publishing: the version you write is the version you submit through a site such as EasyChair or CyberChair and the version that, after correction, will be published. The middlemen have been cut out.

We moved to this system because technology made it possible, and also because of the irresistible lure, for publishers, of saving money (even if, in the long term, they may have removed some of the very reasons for their existence). The consequences of this change go, however, far beyond money.

Integrating change

To understand how fundamentally the stage has changed, let us go back for a moment to the old system. It has many advantages, but also limitations. Some are obvious, such as the amount of work required, involving several people, and the delay from paper completion to paper publication. But in my view the most significant drawback has to do with managing change. If after publication you find a mistake, you must convince the journal to include an erratum: a new mini-article, subject to the same process. That requirement is reasonable enough but the scheme does not support a significant mode of scientific writing: working repeatedly on a single article and progressively refining it. This is not the “LPU” (Least Publishable Unit) style of publishing, but a process of studying an important idea or research project and aiming towards the ideal paper about it by successive approximation. If six months after the original publication of an article you have learned more about the topic and how to present it, the publication strategy is not obvious: resubmit it and risk being accused of self-plagiarism; avoid repetition of basic elements, making the article harder to read independently; artificially increase differences. This conundrum is one of the legitimate sources of the LPU phenomenon: faced with the choice between freezing material and repeating it, people end up publishing it bit by bit.

Now back again to today. If you are a researcher, you want the world to know about your ideas as soon as they are in a clean form. Today you can do this easily: no need to photocopy page after page and lick postage stamps on envelopes the way Dijkstra did; just generate a PDF and put it on your Web page or (to help establish a record if a question of precedence later comes up) on ArXiv. Just to make sure no one misses the information, tweet about it and announce it on your Facebook and LinkedIn pages. Some authors do this once the paper has been accepted, but many start earlier, at the time of submission or even before. I should say here that not all disciplines allow such author behavior; in biology and medicine in particular publishers appear to limit authors’ rights to distribute their own texts. Computer scientists would not tolerate such restrictions, and publishers, whether nonprofit or commercial, largely leave us alone when we make our work available on the Web.

But we are talking about far more than copyright and permissions (in this article I am in any case staying out of these emotionally and politically charged issues, open access and the like, and concentrating on the effect of technology changes on the process of publishing and the publication culture). The very notion of publication has changed. The process part is gone; only the result remains, and that result can be an evolving product, not a frozen artifact.

Particle, or wave?

Another way to describe the difference is that a traditional publication, for example an article published in a journal, is like a particle: an identifiable material object. With the ease of modification, a publication becomes more like a wave, which allows an initial presentation to propagate to successively wider groups of readers:

Maybe you start with a blog entry, then you register the first version of the work as a technical report in your institution or on ArXiv, then you submit it to a workshop, then to a conference, then a version of record in a journal.

In the traditional world of publication each of these would have to be made sufficiently different to avoid the accusation of plagiarism. (There is some tolerance, for example a technical report is usually not considered prior publication, and it is common to submit an extended version of a conference paper to a journal — but the journal will require that you include enough new material, typically “at least 30%.”)

For people who like to polish their work repeatedly, that traditional model is increasingly hard to accept. If you find an error, or a better way to express something, or a complementary result, you just itch to make the change here and now. And you can. Not on a publisher’s site, but on your own, or on ArXiv. After all, one of the epochal contributions of computer technology, not heralded loudly enough, is, as I argued in another blog article [2], the ease with which we can change, extend and refine our creations, developing like a Beethoven and releasing like a Mozart.

The “publication as product” becomes an evolving product, available at every step as a snapshot of the current state. This does not mean that you can cover up your mistakes with impunity: archival sites use “diff” techniques to maintain a dated record of successive versions, so that in case of doubt, or of a dispute over precedence,  one can assess beyond doubt who released what statement when. But you can make sure that at any time the current version is the one you like best. Often, it is better than the official version on the conference or journal site, which remains frozen forever in the form it had on the day of its release.

What then remains of “publication as process”? Not much; in the end, a mere drag-and-drop from the work folder to the publication folder.

Well, there is an aspect I have not mentioned yet.

The sanction

Apart from its material side, now gone or soon to be gone, the traditional publication process has another role: what a recent article in this blog [3] called sanction. You want to publish your latest scientific article in Communications of the ACM not just because it will end up being printed and mailed, but because acceptance is a mark of recognition by experts. There is a whole gradation of prestige, well known to researchers in every particular field: conferences are better than workshops, some journals are as good as conferences or higher, some conferences are far more prestigious than others, and so on.

That sanction, that need for an independent stamp of approval, will remain (and, for academics, young academics in particular, is of ever growing importance). But now it can be completely separated from the publication process and largely separated (in computer science, where conferences are so important today) from the conference process.

Here then is what I think scientific publication will become. The researcher (the author) will largely be in control of his or her own text as it goes through the successive waves described above. A certified record will be available to verify that at time t the document d had the content c. Then at specific stages the author will submit the paper. Submit in the sense of appraisal and, if the appraisal is succesful, certification. The submission may be to a conference: you submit your paper for presentation at this year’s ICSE, POPL or SIGGRAPH. (At the recent Dagstuhl publication culture workshop, Nicolas Holzschuch mentioned that some graphics conferences accept for presentation work that has already been published; isn’t this scheme more reasonable than the currently dominant practice of conference-as-publication?) You may also submit your work, once it reaches full maturity, to a journal. Acceptance does not have to mean that any trees get cut, that any ink gets spread, or even that any bits get moved: it simply enables the journal’s site to point to the article, and your site to add this mark of recognition.

There may also be other forms of recognition, social-network or Trip Advisor style: the community gets to pitch in, comment and assess. Don’t laugh too soon. Sure, scientific publication has higher standards than Wikipedia, and will not let the wisdom of the crowds replace the judgment of experts. But sometimes you want to publish for communication, not sanction; especially if you have the privilege of no longer being trapped in the publish-or-perish race you may simply want to make your research known, and you have little patience for navigating the meanders of conventional publication, genuflecting to the publications of PC members, and following the idiosyncratic conventional structure of the chosen conference community. Then you just publish and let the world decide.

In most cases, of course, we do need the sanction, but there is no absolute reason it should be tied to the traditional structures of journal publication and conference participation. There will be resistance, if only because of the economic interests involved; some of what we know today will remain, albeit with a different focus: conferences, as a place where the best work of the moment is presented (independently of its publication); printed books, as noted;  and printed journals that bring real added value in the form of high-quality printing, layout and copy editing (and might still insist that you put on their site a copy of your paper rather than, or in addition to, a reference to your working version).

The trend, however, is irresistible. Publication is no longer a process, it is a product, increasingly under the control of the authors. As a product it is no longer a defined particle but a wave, progressively improving as it reaches successive classes of readership, undergoes successive steps of refinement and receives, informally from the community and officially from more or less prestigious sources, successive stamps of approval.

References

[1] Dijkstra archive at the University of Texas at Austin, here.

[2] Bertrand Meyer: Computer Technology: Making Mozzies out of Betties, article on this blog, 2 August 2009, available here.

[3] Bertrand Meyer: Conferences: Publication, Communication, Sanction, article on this blog, 6 February 2013, available here.

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

A healthy discussion is taking place in the computer science community on our publication culture. It was spurred by Lance Fortnow’s 2009 article [1]; now Moshe Vardi has taken the lead to prepare a report on the topic, following a workshop in Dagstuhl in November [2]. The present article and one that follows (“The Waves of Publication”)  are intended as contributions to the debate.

One of the central issues is what to do with conferences. Fortnow had strong words for the computer science practice of using conferences as the selective publication venues, instead of relying on journals as traditional scientific disciplines do. The criticism is correct, but if we look at the problem from a practical perspective it is unlikely that top conferences will lose their role as certifiers of quality. This is not a scientific matter but one of power. People in charge of POPL or OOPSLA have decisive sway over the careers (one is tempted to say the lives) of academics, particularly young academics, and it is a rare situation in human affairs that people who have critical power voluntarily renounce it. Maybe the POPL committee will see the light: maybe starting in 2014 it will accept all reasonable papers somehow related to “principles of programming languages”, turn the event itself into a pleasant multi-track community affair where everyone in the field can network, and hand over the selection and stamp-of-approval job to a journal such as TOPLAS. Dream on; it is not going to happen.

We should not, however, remain stuck with the status quo and all its drawbacks. That situation is unsustainable. As a single illustration, consider the requirement, imposed by all conferences, that having a paper pass the refereeing process is not enough: you must also register. A couple of months before the conference, authors of accepted papers (at least, they thought their paper was accepted) receive a threatening email telling them that unless they register and pay their paper will not be published after all. Now assume an author, in a field where a conference is the top token of recognition, has his visa application rejected by the country of the conference — a not so uncommon situation — and does not register. (Maybe he does not mind paying the fee, but he does not want to lie by pretending he is going to attend whereas he knows he will not.) He has lost his opportunity for publication and perhaps severely harmed this career. What have such requirements to do with science?

To understand what can be done, we need to analyze the role of conferences. In an earlier article  [3] I described four “modes and uses” of publication: Publication, Exam, Business and Ritual. From the organizers’ viewpoint, ignoring the Business and Ritual aspects although they do play a significant role, a conference has three roles: Publication, Communication and Sanction. The publication part corresponds to the proceedings of the conference, which makes articles available to the community at large, not just the conference attendees. The communication part only addresses the attendees: it includes the presentation of papers as well as all other interactions made possible by being present at a conference. The sanction part (corresponding to the “exam” part of the more general classification) is the role of a renowned conference as a stamp of approval for the best work of the moment.

What we should do is separate these roles. A conference can play all three roles, but it can also select two of them, or even just one. A well-established, prestigious conference will want to retain its sanctioning role: accepted papers get the stamp of approval. It will also remain an event, where people meet. And it may distribute proceedings. But the three roles can also be untied:

• Publication is the least critical, and can easily be removed from the other two, since everything will be available on the Web. In fact the very notion of proceedings is quickly becoming fuzzy: more and more conferences save money by not distributing printed proceedings to attendees, sometimes not printing any proceedings at all; and some even spare themselves the production of a proceedings-on-a-stick, putting the material on the Web instead. A conference may still decide to have its own proceedings, or it might outsource that part to a journal. Each conference will make these decisions based on its own culture, tradition, ambition and constraints. For authors, the decision does not particularly matter: what counts are the sanction, which is provided by the refereeing process, and the availability of their material to the world, which will be provided in any scenario (at least in computer science where we have, thankfully, the permission to put our papers on our own web sites, an acquired right that our colleagues from other disciplines do not all enjoy).
• Separating sanction from communication is a natural step. Acceptance and participation are two different things.

Conference organizers should not be concerned about lost revenue: most authors will still want to participate in the conference, and will get the funding since institutions are used to pay for travel to present accepted papers; some new participants might come, attracted by more interaction-oriented conference styles; and organizers can replace the requirement to register by a choice between registering and paying a publication fee.

Separating the three roles does not mean that any established conference renounces its sanctioning status, acquired through the hard work of building the conference’s reputation, often over decades. But everyone gets more flexibility. Several combinations are possible, such as:

• Sanction without communication or publication: papers are submitted for certification through peer-review, they are available on the Web anyway, and there is no need for a conference.
• Publication without sanction or communication: an author puts a paper on his web page or on a self-publication site such as ArXiv.
• Sanction and communication without publication: a traditional selective conference, which does not bother to produce proceedings.
• Communication without sanction: a working conference whose sole aim is to advance the field through presentations and discussions, and accepts any reasonable submission. It may be by invitation (a kind of advance sanction). It may have proceedings (publication) or not.

Once we understand that the three roles are not inextricably tied, the stage is clear for removal for some impediments to a more effective publication culture. Some, not all. The more general problem is the rapidly changing nature of scientific publication, what may be called the concentric waves of publication. That will be the topic of the next article.

References

[1] Lance Fortnow: Time for Computer Science to Grow Up, in Communications of the ACM, Vol. 52, no. 8, pages 33-35, 2009, available here.

[2] Dagstuhl: Perspectives Workshop: Publication Culture in Computing Research, see here.

[3] Bertrand Meyer: The Modes and Uses of Scientific Publication, article on this blog, 22 November 2011, see here.

A core aspect of our verification work is the use of “strong” contracts, which express sophisticated specification properties without requiring a separate specification language: even for advanced properties, there is no need for a separate specification language, with special notations such as those of first-order logic; instead, one can continue to rely, in the tradition of Design by Contract, on the built-in notations of the programming language, Eiffel.

This is the idea of domain theory, as discussed in earlier posts on this blog, in particular [1]. An early description of the approach, part of Bernd Schoeller’s PhD thesis work, was [2]; the next step was [3], presented at VSTTE in 2010.

A new paper to be presented at ICSE in May [3], part of an effort led by Nadia Polikarpova for her own thesis in progress, shows new advances in using strong specifications, demonstrating their expressive power and submitting them to empirical evaluation. The results show in particular that strong specifications justify the extra effort; in particular they enable automatic tests to find significantly more bugs.

A byproduct of this work is to show again the complementarity between various forms of verification, including not only proofs but (particularly in the contribution of two of the co-authors, Yi Wei and Yu Pei, as well as Carlo Furia) tests.

References

[1] Bertrand Meyer: Domain Theory: the forgotten step in program verification, article on this blog, see here.

[2] Bernd Schoeller, Tobias Widmer and Bertrand Meyer: Making Specifications Complete Through Models, in Architecting Systems with Trustworthy Components, eds. Ralf Reussner, Judith Stafford and Clemens Szyperski, Lecture Notes in Computer Science, Springer-Verlag, 2006, available here.

[3] Nadia Polikarpova, Carlo Furia and Bertrand Meyer: Specifying Reusable Components, in Verified Software: Theories, Tools, Experiments (VSTTE ‘ 10), Edinburgh, UK, 16-19 August 2010, Lecture Notes in Computer Science, Springer Verlag, 2010, available here.

[4] Nadia Polikarpova, Carlo A. Furia, Yu Pei, Yi Wei and Bertrand Meyer: What Good Are Strong Specifications?, to appear in ICSE 2013 (Proceedings of 35th International Conference on Software Engineering), San Francisco, May 2013, draft available here.

As part of a Festschrift volume for Martin Glinz of the university of Zurich I wrote a paper [1] describing a general approach to requirements that I have been practicing and developing for a while, and presented in a couple of talks. The basic idea is to rely on object-oriented techniques, including contracts for the semantics, and to weave several levels of discourse: natural-language, formal and graphical.

Reference

[1] Bertrand Meyer: Multirequirements, to appear in Martin Glinz Festschrift, eds. Anne Koziolek and Norbert Scheyff, 2013, available here.

The European Software Engineering Conference takes place every two years in connection with the ACM Foundations of Software Engineering symposium (which in even years is in the US). The next ESEC/FSE  will be held for the first time in Russia, where it will be the first major international software engineering conference ever. It comes at a time when the Russian software industry is ever more present through products and services offered worldwide. See the conference site here. The main conference will be held 21-23 August 2013, with associated events before and after so that the full dates are August 18 to 26. (I am the general chair.)

Other than ICSE, ESEC/FSE is second to none in the quality of the program. We already have four outstanding keynote speakers:  Georges Gonthier from Microsoft Research, Paola Inverardi from L’Aquila in Italy, David Notkin from U. of Washington (in whose honor a symposium will be held as an associated event of ESEC/FSE, chaired by Michael Ernst), and Moshe Vardi of Rice and of course Communications of the ACM.

Saint Petersburg is one of the most beautiful cities in the world, strewn with gilded palaces, canals, world-class museums (not just the Hermitage), and everywhere mementos of the great poets, novelists, musicians and scientists who built up its fame.

Hosted by ITMO National Research University, the conference will be held in the magnificent building of the Razumovsky Palace on the banks of the Moika river; see here.

The Call for Papers has a deadline of March 1st, so there is still plenty of time to polish your best paper and send it to ESEC/FSE. There is also still time to propose worskhops and other associated events. ESEC/FSE will be a memorable moment for the community and we hope to see many of the readers there.

In modeling object-oriented programs, for purposes of verification (proofs) or merely for a better understanding, we are faced with the unique “general relativity” property of OO programming: all the operations you write (excluding non-OO mechanisms such as static functions) are expressed relative to a “current object” which changes repeatedly during execution. More precisely at the start of a call x.r (…) and for the duration of that call the current object changes to whatever x denotes — but to determine that object we must again interpret x in the context of the previous current object. This raises a challenge for reasoning about programs; for example in a routine the notation f.some_reference, if f is a formal argument, refers to objects in the context of the calling object, and we cannot apply standard rules of substitution as in the non-OO style of handling calls.

In earlier work [1, 2] initially motivated by the development of the Alias Calculus, I introduced a notion of negative variable to deal with this issue. During the execution of a call x.r (…) the negation of x , written x’, represents a back pointer to the calling object; negative variables are characterized by axiomatic properties such as x.x’= Current and x’.(old x)= Current. Alexander Kogtenkov has implemented these ideas and refined them.

In a recent paper under submission [3], we review the concepts and applications of negative variables.

References

[1] Bertrand Meyer: Steps Towards a Theory and Calculus of Aliasing, in International Journal of Software and Informatics, 2011, available here.

[2] Bertrand Meyer: Towards a Calculus of Object Programs, in Patterns, Programming and Everything, Judith Bishop Festschrift, eds. Karin Breitman and Nigel Horspool, Springer-Verlag, 2012, pages 91-128, available here.

[3] Bertrand Meyer and Alexander Kogtenkov: Negative Variables and the Essence of Object-Oriented Programming, submitted for publication, 2012. [Updated 13 January 2014: I have removed the link to the draft mentioned in this post since it is now superseded by the new version, soon to be published, and available here.]

Actually it is not a musical but an extensive survey. I have long been fascinated by the notion of loop invariant, which describes the essence of a loop. Considering a loop without its invariant is like conducting an orchestra without a score.

In this submitted survey paper written with Sergey Velder and Carlo Furia [1], we study loop invariants in depth and describe many algorithms from diverse areas of computer science through their invariants. For simplicity and clarity, the specification technique uses the Domain Theory technique described in an earlier article on this blog [2] (see also [3]). The invariants were verified mechanically using Boogie, a sign of how much more realistic verification technology has become in recent years.

The survey was a major effort (we worked on it for a year and a half); it is not perfect but we hope it will prove useful in the understanding, teaching and verification of important algorithms.

Here is the article’s abstract:

At the heart of every loop, and hence of all significant algorithms, lies a loop invariant: a property ensured by the initialization and maintained by every iteration so that, when combined with the exit condition, it yields the loop’s final effect. Identifying the invariant of every loop is not only a required step for software verification, but also a key requirement for understanding the loop and the program to which it belongs. The systematic study of loop invariants of important algorithms can, as a consequence, yield insights into the nature of software.

We performed this study over a wide range of fundamental algorithms from diverse areas of computer science. We analyze the patterns according to which invariants are derived from postconditions, propose a classification of invariants according to these patterns, and present its application to the algorithms reviewed. The discussion also shows the need for high-level specification and invariants based on “domain theory”. The included invariants and the corresponding algorithms have been mechanically verified using an automatic program prover. Along with the classification and applications, the conclusions include suggestions for automatic invariant inference and general techniques for model-based specification.

References

[1] Carlo Furia, Bertrand Meyer and Sergey Velder: Loop invariants: analysis, classification, and examples, submitted for publication, December 2012, draft available here.

[2] Domain Theory: the Forgotten Step in Program Verification, article from this blog, 11 April 2012, available here.

[3] Domain Theory: Precedents, article from this blog, 11 April 2012, available here

It is a frequent complaint that production software contains too many features: “I use only  maybe 5% of Microsoft Word!“, with the implication that the other 95% are useless, and apparently without the consideration that maybe someone else needs them; how do you know that what is good enough for you is good enough for everyone?

The agile literature frequently makes this complaint against “software bloat“, and has turned it into a principle: build minimal software.

Is software really bloated? Rather than trying to answer this question it is useful to analyze where features come from. In my experience there are three sources: internal ideas; suggestions from the field; needs of key customers.

1. Internal ideas

A software system is always devised by a person or group, who have their own views of what it should offer. Many of the more interesting features come from these inventors and developers, not from the market. A competent group does not wait for users or prospects to propose features, but comes up with its own suggestions all the time.

This is usually the source of the most innovative ideas. Major breakthroughs do not arise from collecting customer wishes but from imagining a new product that starts from a new basis and proposing it to the market without waiting for the market to request it.

2. Suggestions from the field

Customers’ and prospects’ wishes do have a crucial role, especially for improvements to an existing product. A good marketing department will serve as the relay between the field’s wishes and the development team. Many such suggestions are of the “Check that box!” kind: customers and particularly prospects look at the competition and want to make sure that your product does everything that the others do. These suggestions push towards me-too features; they are necessary to keep up with the times, but must be balanced with suggestions from the other two sources, since if they were the only inspiration they would lead to a product that has the same functionality as everyone else’s, only delivered a few months later, not the best recipe for success.

3. Key customers

Every company has its key customers, those who give you so much business that you have to listen to them very carefully. If it’s Boeing calling, you pay more attention than to an unknown individual who has just acquired a copy. I suspect that many of the supposedly strange features, of products the ones that trigger “why would anyone ever need this?” reactions, simply come from a large customer who, at some point in the product’s history, asked for a really, truly, absolutely indispensable facility. And who are we — this includes Microsoft and Adobe and just about everyone else — to say that it is not required or not important?

It is easy to complain about software bloat, and examples of needlessly complex system abound. But your bloat may be my lifeline, and what I dismiss as superfluous may for you be essential. To paraphrase a comment by Ichbiah, the designer of Ada, small systems solve small problems. Outside of academic prototypes it is inevitable that  a successful software system will grow in complexity if it is to address the variety of users’ needs and circumstances. What matters is not size but consistency: maintaining a well-defined architecture that can sustain that growth without imperiling the system’s fundamental solidity and elegance.

After several months of inaction I have updated my “Gallery of Computer Scientists” [1]. It benefits from many recent meetings where the density per square meter of Turing award winners and other brilliant computer scientists was hard to beat, most notably the two extraordinary Turing centenary celebrations  — the ACM event in San Francisco, and Andrei Voronkov’s Manchester conference — and our own LASER summer school of last September which brought together the Gotha of programming language designers. And I still have not included everyone.

I do not know of any photographic collection anywhere that compares to this archive in either quantity or quality of the scientists pictured. My only regret is that I did not start earlier (I missed several giants of the field, to soon departed, such as Dijkstra, Dahl and Nygaard, even though I had many occasions to photograph them). The truth is that I had got impatient with photography and started again only when digital cameras became widely available.

The quality of the pictures themselves varies. It is definitely higher in recent ones: I may have become a better photographer, but it does not hurt that I have more sophisticated cameras than the rudimentary point-and-shoot I was using at the beginning. I should also improve the layout of the page, although I hope you will appreciate the ability to move the cursor around to get large pictures without having to click and go to different pages.

I started this collection because it occurred to me that for a number of reasons I am, more than almost anyone I know, in the position of meeting outstanding people from many different sub-communities of software engineering and the rest of computer science: from program verification, semantics, languages, algorithms to architecture, management, empirical software engineering and many others. I realized that it would be unconscionable not to take advantage of these opportunities and do for computer scientists what Paul Halmos did for mathematicians [2].

Some of the people pictured are more famous than others, but all do interesting work. There is no profound logic to the choice of subjects; it obviously depends on the chances I get, but also on the time I can spend afterwards to sort through the shots (this is not a full-time job). So if you know I took a picture of you and you do not see it on the page, do not take offense: it may be a matter of time, or I may need another opportunity and a better shot.

All the pictures are by me. They are of different styles; I try to capture a personality and a mood. Many shots show a computer scientist in flagrante delicto: doing computer science, as when giving a talk, or engaging in a design discussion around a laptop. Some were taken in more informal settings, such as a long winter walk in the woods. A few reveal some humorous or fancy aspect of the subject’s personality. None has any context or explanation; I will not tell you, for example, why Tony Hoare had, on that day, two hats and two umbrellas. I think it is more fun to let you imagine.

Pictures are only pictures and what matters is the work that all these great people do. Still, I hope you will enjoy seeing what they look like.

References

[1] Bertrand Meyer’s Gallery of Computer Scientists, available here.
[2] Paul Halmos’s photo collection, see here.

A couple of weeks ago I proposed a small quiz. (I also stated that the answer would come “on Wednesday” — please understand any such promise as “whenever I find the time”. Sorry.) Here is the answer.

The quiz was:

I have a function:

• For 0 it yields 0.
• For 1 it yields 1.
• For 2 it yields 4.
• For 3 it yields 9.
• For 4 it yields 16.

What is the value for 5?

Shortly thereafter I added a hint: the value for 5 is 25, and changed the question to: “What is the value for 6?”. For good measure we can also ask about the value for 1000. Now compare your answer to  what follows.

A good answer for the value at 6 is: 34 . The function in this case is -10 + 5 x + |2 x – 3| + |2 x -7|. It matches the values for the given inputs.

The value for 1000 is 8980:

Another good answer at position 6 is 35.6. It comes up if we assume the function is over reals rather than integers; then a possible formula, which correlates very well (R-square of 0.9997) with the values at the given inputs, is:

869.42645566111 (1 – 0.4325853145802 e^{-.0467615868913719  (x – 17.7342512233011)})^{2.3116827277657443}

with a quite different asymptotic behavior, giving the value 869.4 at position 1000:

Some readers might have thought of another possibility, the square function x², which again matches all the given values:

So which of these answers is right? Each is as good as the others, and as bad. There is in particular no reason to believe that the values given in the quiz’s statement suggest the square function. Any function that fits the given values, exactly (if we stick to integers) or approximately (with reals as simulated on a computer) is an equally worthy candidate. Six inputs, or six thousand, do not resolve the question. At best they are hints.

This difference between a hint and a solution is at the core of software engineering. It is, for example, the difference between a test and a specification. A test tells us that the program works for some values; as Dijkstra famously pointed out, and anyone who has developed a serious program has experienced, it does not tell us that it will work for others. The more successful tests, the more hints; but they are still only hints. I have always wondered whether Dijkstra was explicitly thinking of the Popperian notion of falsifiability: no number of experiments will prove a physical theory (although a careful experiment may boost the confidence in the theory, especially if competing theories fail to explain it, as the famous Eddington expedition did for relativity in 1919 [1]); but a single experiment can disprove a theory. Similarly, being told that our function’s value at 6 is 34 disqualifies the square function and the last one (the exponential), but does not guarantee that the first function (the linear combination) is the solution.

The specification-testing duality is the extension to computer science of the basic duality of logic. It starts with the elementary boolean operators: to prove a or b it suffices to establish a or to establish b; and to disprove a and b it suffices to show that a does not hold or to show that b does not hold. The other way round, to disprove a or b we have to show that a does not hold and to show that b does not hold; to prove that a and b holds, we have to show that a holds and to show that b holds.

Predicate calculus generalizes or to ∃, “there exists”, and and to ∀, “for all”. To prove ∃ x | p (x) (there is an x of which p holds) it suffices to find one value a such that p (a); let’s be pretentious and say we have “skolemized” x. To disprove∀ x | p (x) (p holds of all x) it suffices to find one value for which p does not hold.

In software engineering the corresponding duality is between proofs and tests, or (equivalently) specifications and use cases. A specification is like a “for all”: it tells us what must happen for all envisioned inputs. A test is like a “there exists”: it tells us what happens for a particular input and hence, as in predicate calculus, it is interesting as a disproof mechanism:

• A successful test brings little information (like learning the value for 5 when trying to figure out what a function is, or finding one true value in trying to prove a ∀ or a false value in trying to prove a ∃).
• An unsuccessful test brings us decisive information (like a false value for a ∀): the program is definitely not correct. It skolemizes incorrectness.

A proof, for its part, brings the discussion to an end when it is successful. In practice, testing may still be useful in this case, but only testing that addresses issues not covered by the proof:

• Correctness of the compiler and platform, if not themselves proved correct.
• Correctness the proof tools themselves, since most practical proofs require software support.
• Aspects not covered by the specification such as, typically, performance and usability.

But for the properties it does cover the proof is final.

It is as foolish, then, to use tests in lieu of specifications as it would be to ignore the limitations of a proof. Agile approaches have caused much confusion here; as often happens in the agile literature [2], the powerful insight is mixed up with harmful advice. The insight, which has significantly improved the practice of software development, is that the regression test suite is a key asset of a project and that tests should be run throughout. The bad advice is to ditch upfront requirements and specifications in favor of tests. The property that tests lack and specifications possess is generality. A test is an instance; a thousand tests can never be more than a thousand instances. As I pointed out in a short note in EiffelWorld (the precursor to this blog) a few years ago [3], the relationship is not symmetric: one can generate tests from a specification, but not the other way around.

The same relationship holds between use cases and requirements. It is stunning to see how many people think that use cases (scenarios) are a form of requirements. As requirements they are as useless as one or ten values are to defining a function. Use cases are a way to complement the requirements by describing the system’s behavior in selected important cases. A kind of reality check, to ensure that whatever abstract aims have been defined for the system it still covers the cases known to be of immediate interest. But to rely on use cases as requirements means that you will get a system that will satisfy the use cases — and possibly little else.

When I use systems designed in recent years, in particular Web-based systems, I often find myself in a stranglehold: I am stuck with the cases that the specifiers thought of. Maybe it’s me, but my needs tend, somehow, to fall outside of these cases. Actually it is not just me. Not long ago, I was sitting close to a small-business owner who was trying to find her way through an insurance site. Clearly the site had a planned execution path for employees, and another for administrators. Problem: she was both an employee and the administrator. I do not know how the session ended, but it was a clear case of misdesign: a system built in terms of standard scenarios. Good specification performs an extra step of abstraction (for example using object-oriented techniques and contracts, but this is for another article). Skipping this step means forsaking the principal responsibility of the requirements phase: to generalize from an analysis of the behavior in known cases to a definition of the desired behaviors in all relevant cases.

Once more, as everywhere else in computer science [4], abstraction is the key to solid results that stand the test of time. Definitely better than judging a book by its cover, inferring a function by its first few values, verifying a program by its tests, or specifying a system by its use cases.

References

[1] See e.g. a blog article: Einstein and Eddington, here.

[2] Bertrand Meyer: Agile! The Good, the Hype and the Ugly, 2013, to appear.

[3] Bertrand Meyer: Test or spec? Test and spec? Test from spec!, EiffelWorld column, 2004 available here.

[4] Jeff Kramer: Is Abstraction the Key to Computer Science?, in Communications of the ACM, vol. 50, no. 4, April 2007, pages 36-42,  available from CiteSeer here

Alexander Kogtenkov pointed out to me that precursor work to my papers on the Alias Calculus [1] [2] had been published by John Whaley and Martin Rinard [3]. There are some significant differences; in particular my rules are simpler, and their work is not explicitly presented as a calculus. But many of the basic ideas are the same. The reason I did not cite that paper is simply that I was not aware of it; I am happy to correct the omission.

References

[1] Bertrand Meyer: Towards a Theory and Calculus of Aliasing, in Journal of Object Technology, vol. 9, no. 2, March-April 2010, pages 37-74, available here (superseded by [2])
[2] Bertrand Meyer: Steps Towards a Theory and Calculus of Aliasing, in International Journal of Software and Informatics, 2011, available here (revised and improved version of [1].)
[3] John Whaley and Martin Rinard: Compositional Pointer and Escape Analysis for Java Programs, in POPL 1999, available here.

For various reasons there have been no articles in recent weeks; now we are restarting on a regular basis!

A the first topic for this new season, here is a little quiz. I have a function:

• For 0 it yields 0.
• For 1 it yields 1.
• For 2 it yields 4.
• For 3 it yields 9.
• For 4 it yields 16.

The question: what is the value for 5?

Answer next Wednesday (at least it will be Wednesday in some time zone).

I came across an obscure and surprisingly interesting article by Cliff Jones [1], about the history of rely-guarantee but with the following extract:

It was perhaps not fully appreciated at the time of [Hoare’s 1969 axiomatic semantics paper] that the roles of pre and post conditions differ in that a pre condition gives permission to a developer to ignore certain possibilities; the onus is on a user to prove that a component will not be initiated in a state that does not satisfy its pre condition. In contrast a post condition is an obligation on the code that is created according to the specification. This Deontic view carries over [to rely-guarantee reasoning].

I use words more proletarian than “deontic”, but this view is exactly what stands behind the concepts of Design by Contract and has been clearly emphasized in all Eiffel literature ever since the first edition of OOSC. It remains, however, misunderstood outside of the Eiffel community; many people confuse Design by Contract with its opposite, defensive programming. The criterion is simple: if you have a precondition to a routine, are you willing entirely to forsake the corresponding checks (conditionals, exceptions…) in the routine body? If not, you may be using the word “contract” as a marketing device, but that’s all. The courage to remove the checks is the true test of adulthood.

The application of Microsoft’s “Code Contracts” mechanism to the .NET libraries fails that test: a precondition may say “buffer not full” or “insertions allowed”, but the code still checks the condition and triggers an exception. The excuse I have heard is that one cannot trust those unwashed developers. But the methodological discipline is lost. Now let me repeat this using clearer terminology: it’s not deontic.

Reference

[1] Cliff Jones: The role of auxiliary variables in the formal development of concurrent programs, in Reflections on the work of C. A. R. Hoare, eds. Jones, Roscoe and Wood, Springer Lecture Notes in Computer Science,  2009, technical report version available here.

Since release 6.2 (November 2008) EiffelStudio has included the EIS system, Eiffel Information System. It has been regularly revised, and significantly improved for the recent 7.1 release.

For us EIS is a key contribution with far-reaching software engineering implications, but many users seem unaware of it, perhaps because we have not been explicit enough about why we think it is important. We would love to have more people try it and give us their feedback. (Please make sure to use the 7.1 version.) Information on EIS can be found in the documentation [1] and also in a blog entry by Tao Feng [2].

EIS connects an Eiffel system with external documents in arbitrary formats; examples of formats currently supported are Microsoft Word and PDF, but you can easily add protocols. Such a connection links an element of the Eiffel text, such as a feature, with an element of the external document, such as a paragraph. Then clicking the Eiffel element in EiffelStudio will open the document at the corresponding place in the external tool (Word, Acrobat etc.); this is the EIS “outgoing” mechanism. Conversely the external element has a back link: clicking in the external tool will open EiffelStudio at the right place; this is the EIS “incoming” mechanism.

For the outgoing mechanism, the link will appear as part of a note clause (with attributes filled by default, you need only edit the URL and any option that you wish to change):

The fundamental idea behind EIS is to support the seamless form of software development promoted and permitted by Eiffel, where all phases of a project’s lifecycle are closely linked and the code provides the ultimate reference. Since other documents are often involved, in particular a requirements document (SRS, Software Requirements Specification), it is essential to record their precise associations with elements of the software text. For example a paragraph in the SRS could state that “Whenever the tank temperature reaches 50 degrees, the valve shall be closed”. In the software text, there will be some feature, for example monitor_temperature in the class TANK, reflecting this requirement. The two elements should be linked, in particular to ensure that dependencies appear clearly and that any change in either the requirements or the code triggers the corresponding update to the other side. This is what EIS provides.

We envision further tools to track dependencies and in particular to warn users if an element of a connection (e.g. requirement or code) changes, alerting them to the need to check the linked elements on the other side. One of the key goals here is traceability: effective project management, particular during the evolution of a system, requires that all dependencies between the project’s artifact are properly recorded so that it is possible to find out the consequences of any change, proposed or carried out.

The general approach reflects the essential nature of Eiffel development, with its Single Product Principle linking all elements of a software system and minimizing, rather than exaggerating, the inevitable differences of levels of abstraction between requirements, design, code, test plans, test logs, schedules and all the other products of a software project. The core problem of software engineering is change: if we use different tools and notations at each step, and keep the documents separate, we constantly run the risk of divergence between intent and reality. Eiffel by itself offers a good part of the solution by providing a single method (with all its principles, from Design by Contract to open-closed etc.), a single notation (the Eiffel language itself) and a single integrated set of tools (the EiffelStudio IDE) supporting the entire lifecycle; the language, in particular is meant for requirements and design as much as for implementation. The graphical forms (BON and UML, as produced by the Diagram Tool of EiffelStudio in a roundtrip style, i.e. changes to the diagram immediately generate code and changes to the code are reflected in the diagram) directly support these ideas. Of course documents in other formalisms, for example SRS, remain necessary for human consumption; but they should be closely linked to the core project asset, the Eiffel code; hence the need for EIS and its connection mechanisms.

This approach, as I have often noted when presenting it in public, is hard to convey to people steeped in the mindset of the past (UML as separate from code, model-driven development) which magnify the differences between software levels, hence introducing the risk of divergence and making change painful. The Eiffel approach is innovative enough to cause incomprehension or even rejection. (“What, you are not model-driven, but everyone says model-driven is good!” – well, models are bad if they are inaccurate. In the Eiffel approach the model and the program are the same thing, or more precisely the model is the abstract view of the program, obtained through abstraction mechanisms such as deferred classes with contracts and the “contract view” tool of EiffelStudio.)

To be effective, these ideas require proper tool support, for which EIS is a start. But we would like to know if we are on the right track and hence need feedback. We would be grateful if you could try out EIS and tell us what you think, both about the current state of the mechanism and its long-term prospects in the general framework of high-quality, sustainable software development.

References

[1] EIS documentation, here.

[2] Tao Feng, Start using Eiffel Information System, Eiffelroom blog entry of 17 April 2008, available here.

Both Gary Leavens and Jim Horning commented (partly here, partly on Facebook) about my Domain Theory article [1] to mention that Larch had mechanisms for domain modeling and specification reuse. As Horning writes:

The Larch Shared Language was really all about creating reusable domain theories, including theorems about the domains.  See, for example [2] and [3].

I am honored that they found the time to write about the article and happy to acknowledge Larch, one of the most extensive efforts, over several decades, to provide serious notations and tools for specification. Leavens’s and Horning’s messages gave me the opportunity to re-read some Larch papers and discover a couple I did not know.

My article did not try to provide exhaustive references; if it had, Larch would have been among them. I would probably have cited my own paper on M [4], earlier than [3], which introduces a notation for composing specifications; see section 1.4 (“Features of the M method and the associated notation have thus been devised to allow for modular descriptions of systems. A system description may include an interface paragraph that describes the connection of the current specification with others, existing or yet to be written”) and the  presentation of these mechanisms in section 5.

Larch traits, described in [3], pursue a similar aim, but the earlier article cited by Horning [2] is a general, informal discussion of formal specification; it does not mention traits, and in fact does not cite Larch, stating instead “We have experimented with the use of two very different tools, PIE and Affirm, in constructing modest sized algebraic specifications”. Its general observations about the specification task remain useful today, and it does mention reuse in passing.

If we were to look for precedents, the basic source would have to be the Clear specification language of Goguen and Burstall, for which the citations [5, 6, 7] all appear in my M paper [4] and go back further: 1977-1981. Clear made a convincing case for modularizing specifications, and defined supporting language constructs.

Since these early publications, many people have come to realize that reuse and composition can be as useful on the specification side as they are for programming. Typical specification and verification techniques, however, do not take advantage of this idea and tend to make us restart every time from the lowest level. Domain Theory, as outlined in [1], is intended to bring abstraction, which has proved so beneficial in other parts of software engineering, to the world of specification.

References

[1] Domain Theory: The Forgotten step in program verification, an article in this blog, see here.

[2] John V. Guttag, James J. Horning, Jeannette M. Wing: Some Notes on Putting Formal Specifications to Productive Use, in Science of Computer Programming, vol. 2, no. 1, 1982, pages 53-68. (BM note: I found a copy here.)

[3] John V. Guttag, James J. Horning: A Larch Shared Language Handbook, in Science of Computer Programming, vol. 6, no. 2, 1986, pages 135-157. (BM note: I found a copy here, which also has a link to the Larch report.)

[4] Bertrand Meyer: M: A System Description Method, Technical Report TR CS 85-15, University of California, Santa Barbara, 1985, available here.

[5] Rod M. Burstall and Joe A. Goguen: Putting Theories Together to Make Specifications, in Proceedings of 5th International Joint Conference on Artificial Intelligence, Cambridge (Mass.), 1977, pages 1045- 1058.

[6] Rod M. Burstall and Joe A. Goguen: “The Semantics of Clear, a Specification Language,” in Proceedings of Advanced Course on Abstract Software Specifications, Copenhagen, Lecture Notes on Computer Science 86, Copenhagen, Springer-Verlag, 1980, pages 292-332, available here.

[7] Rod M. Burstall and Joe A. Goguen: An Informal Introduction to Specifications using Clear, in The Correctness Problem in Computer Science, eds R. S. Boyer and JJ. S. Moore, Springer-Verlag, 1981, pages 185-213.

In  last Thursday’s session of the seminar, Kokichi Futatsugi’s talk took longer than planned (and it would have been a pity to stop him), so I postponed my own talk on Automatic inference of frame conditions through the alias calculus to next week (Thursday local date). As usual it will be broadcast live.

Seminar page: here, including the link to follow the webcast.

Time and date: 5 April 2012, 18 Saint Petersburg time; you can see the local time at your location here.

Abstract:

Frame specifications, the description of what does not change in a routine call, are one of the most annoying components of verification, in particular for object-oriented software. Ideally frame conditions should be inferred automatically. I will present how the alias calculus, described in recent papers, can address this need.

There may be a second talk, on hybrid systems, by Sergey Velder.

The Saint Petersburg software engineering seminar has two sessions today (29 March 2012, 18 local time, see here for the date and time in your area), broadcast live:

• By Kokichi Futatsugi from KAIST (Japan): Combining Inference and Search in Verification with CafeOBJ.
• By me: Automatic inference of frame conditions through the alias calculus.

See details including the link for the live webcast on the seminar page. The page also includes links to video recordings of recent sessions.

In the Eiffel user discussion group [1], Ian Joyner recently asked:

A lot of people are now using Result as a variable name for the return value in many languages. I believe this first came from Eiffel, but can’t find proof. Or was it adopted from an earlier language?

Proof I cannot offer, but certainly my recollection is that the mechanism was an original design and not based on any previous language. (Many of Eiffel’s mechanisms were inspired by other languages, which I have always acknowledged as precisely as I could, but this is not one of them. If there is any earlier language with this convention — in which case a reader will certainly tell me — I was and so far am not aware of it.)

The competing conventions are a return instruction, as in C and languages based on it (C++, Java, C#), and Fortran’s practice, also used in Pascal, of using the function name as a variable within the function body. Neither is satisfactory. The return instruction suffers from two deficiencies:

• It is an extreme form of goto, jumping out of a function from anywhere in its control structure. The rest of the language sticks to one-entry, one-exit structures, as I think all languages should.
• In most non-trivial cases the return value is not just a simple formula but has to be computed through some algorithm, requiring the declaration of a local variable just to denote that result. In every case the programmer must invent a name for that variable and, in a typed language, include a declaration. This is tedious and suggests that the language should take care of the declaration for the programmer.

The Fortran-Pascal convention does not combine well with recursion (which Fortran for a long time did not support). In the body of the function, an occurrence of the function’s name can denote the result, or it can denote a recursive call; conventions can be defined to remove the ambiguity, but they are messy, especially for a function without arguments: in function f, does the instruction

f := f + 1

add one to the value of the function’s result as computed so far, as it would if f were an ordinary variable, or to the result of calling f recursively?

Another problem with the Fortran-Pascal approach is that in the absence of a language-defined rule for variable initialization a function can return an undefined result, if some path has failed to initialize the corresponding variable.

The Eiffel design addresses these problems. It combines several ideas:

• No nesting of routines. This condition is essential because without it the name Result would be ambiguous. In all Algol- and Pascal-like languages it was considered really cool to be able to declare routines within routines, without limitation on the depth of recursion. I realized that in an object-oriented language such a mechanism was useless and in fact harmful: a class should be a collection of features — services offered to the rest of the world — and it would be confusing to define features within features. Simula 67 offered such a facility; I wrote an analysis of inter-module relations in Simula, including inheritance and all the mechanisms retained from Algol such as nesting (I am trying to find that document, and if I do I will post it in this blog); my conclusion was the result was too complicated and that the main culprit was nesting. Requiring classes to be flat structures was, in my opinion, one of the most effective design decisions for Eiffel.
• Language-defined initialization. Even a passing experience with C and C++ shows that uninitialized variables are one of the major sources of bugs. Eiffel introduced a systematic rule for all variables, including Result, and it is good to see that some subsequent languages such as Java have retained that convention. For a function result, it is common to ignore the default case, relying on the standard initialization, as in if “interesting case” then Result:= “interesting value” end without an else clause (I like this convention, but some people prefer to make all cases explicit).
• One-entry, one-exit blocks; no goto in overt or covert form (break, continue etc.).
• Design by Contract mechanisms: postconditions usually need to refer to the result computed by a function.

The convention is then simple: in any function, you can use a language-defined local variable Result for you, of the type that you declared for the function result; you can use it as a normal variable, and the result returned by any particular call will be the final value of the variable on exit from the function body.

The convention has been widely imitated, starting with Delphi and most recently in Microsoft’s “code contracts”, a kind of poor-man’s Design by Contract emulation, achieved through libraries; it requires a Result notation to denote the function result in a postcondition, although this notation is unrelated to the mechanisms in the target languages such as C#. As the example of Eiffel’s design illustrates, a programming language is a delicate construction where all elements should fit together; the Result convention relies on many other essential concepts of the language, and in turn makes them possible.

Reference

[1] Eiffel Software discussion group, here.