The one sure way to advance software engineering

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Airplanes today are incomparably safer than 20, 30, 50 years ago: 0.05 deaths per billion kilometers. That’s not by accident.

Rather, it’s by accidents. What has turned air travel from a game of chance into one of the safest modes of traveling is the relentless study of crashes and other mishaps. In the US the National Transportation Safety Board has investigated more than 110,000 accidents since it began its operations in 1967. Any accident must, by law, be investigated thoroughly; airplanes themselves carry the famous “black boxes” whose only purpose is to provide evidence in the case of a catastrophe. It is through this systematic and obligatory process of dissecting unsafe flights that the industry has made almost all flights safe.

Now consider software. No week passes without the announcement of some debacle due to “computers” — meaning, in most cases, bad software. The indispensable Risks forum [1] and many pages around the Web collect software errors; several books have been devoted to the topic.

A few accidents have been investigated thoroughly; two examples are Nancy Leveson’s milestone study of the Therac-25 patient-killing medical device [2], and Gilles Kahn’s analysis of the Ariane 5 crash (which Jean-Marc Jézéquel and I used as a basis for our 1997 article [3]). Both studies improved our understanding of software engineering. But these are exceptions. Most of what we have elsewhere is made of hearsay and partial information, and plain urban legends (like the endlessly repeated story about the Venus probe that supposedly failed because a period was typed instead of a comma — most likely a canard).

Software disasters continue; they attract attention when they arise, and inevitably some kind of announcement is made that the problem is being corrected, or that a committee will study the causes; almost as inevitably, that is the last we hear of it. In the latest issue of Risks alone, you can find several examples (such as [4]). In the past months, breakdowns at Skype, Google and Twitter made headlines; we all learned about the failures, but have you seen precise analyses of what actually happened?

As another typical example, we heard a few months ago from the French press that an “IT error” (une erreur informatique) led to overestimating the pensions of about a million people; since (strangely!)  no one was suggesting that they would be asked to pay the money back, the cost to taxpayers will be over 300 million euros. I looked in vain for any follow-up story: what happened? What was the actual error? Were the tools at fault? The quality assurance procedures? The programmers’ qualifications? Or was it a matter of bad deployment? Of erroneous data, and if so, what was the process for validating inputs? And so on. Most likely we will never know.

But we should know. Especially with public money, any such incident should have a post-mortem, with experts called in (surely at a fraction of the cost of the failure) to analyze what happened and produce a public report.

At least this was a public project, for which some disclosure was inevitable. The software engineering community buzzes with unconfirmed reports of huge software-induced errors, that go unreported because they happen in private companies eager to avoid bad publicity. It’s as if we had allowed aircraft manufacturers, decade after decade, to keep mum about accidents. Where then would air travel safety be today?

Progress in software engineering will come from many sources. Research is critical, including on topics which today appear exotic. But if anyone is looking for one practical, low-tech idea that has an iron-clad guarantee of improving software engineering, here it is: pass a law that requires extensive professional analysis of any large software failure.

The details are not so hard to refine. The initiative would probably have to start at the national level; any industrialized country could be the pioneer. (Or what about Europe as whole?) The law would have to define what constitutes a “large” failure; for example it could be any failure that may be software-related and has resulted in loss either of human life or of property beyond a certain threshold, say $50 million. In the latter case, to avoid accusations of government meddling in private matters, the law could initially be limited to cases involving public money; when it has shown its value, it could then be extended to private failures as well. Even with some limitations, such a law would have a tremendous effect. Only with a thorough investigation of software projects gone wrong can we help the majority of projects to go right.

We can no longer afford to let the IT industry get away with covering up its failures. Lobbying for a Software Incident Full Disclosure Law is the single most important step we can take today to make the world’s software better.

Note (2011)

Later articles have come back to the theme discussed here, and there will probably be more in the future as it remains ever current. They can be found by selecting the tag “Advance”.

References

[1] Peter G. Neumann, moderator: The Risks Digest Forum on Risks to the Public in Computers and Related Systems, available online (going back to 1985!).

[2] Nancy Leveson: Medical Devices: The Therac-25, extract from her book Safeware: System Safety and Computers, Addison-Wesley, 1995, available online.

[3] Jean-Marc Jézéquel and Bertrand Meyer: Design by Contract: The Lessons of Ariane, in Computer (IEEE), vol. 30, no. 1, January 1997, pages 129-130, also available here.

[4] Monty Solomon: Computer Error Caused Rent Troubles for Public Housing Tenants, in Risks 25.76, 15 August 2009, see here.

[5] Une erreur informatique à 300 millions d’euros, in Le Point, 12 May 2009, available here.

 

Specifying user interfaces

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Many blogs including this one rely on the WordPress software. In previous states of the present page you may have noticed a small WordPress bug, which I find interesting.

“Tags” are a nifty WordPress feature. When you post a message, you can specify one or more informative “tags”. The tags of all messages appear in the right sidebar, each with a smaller or bigger font size depending on the number of messages that specified it. You can click such a tag in the sidebar and get, on the left, a page containing all the associated messages.

Now assume that many posts use a particular tag; in our example it is “Design by Contract”, not unexpected for this blog. Assume further that the tag name is long. It is indeed in this case: 18 characters. As a side note, no problem would arise if I used normal spaces in the name, which would then appear on two or three lines; precisely to avoid this  I use HTML “non-breaking spaces”. This is probably not in the WordPress spirit, but any other long tag without spaces would create the same problem. That problem is a garbled display:

dbc_overflows

The long tag overflows the bluish browser area assigned to tags, producing an ugly effect. This behavior is hard to defend: either the tag should have been rejected as too long when the poster specified it or it should fit in its zone, whether by truncation or by applying a smaller font.

I quickly found a workaround, not nice but good enough: make sure that some short tag  (such as “Hoare”) appears much more often than the trouble-making tag. Since font size indicates the relative frequency of tags, the long one will be scaled down to a smaller font which fits.

Minor as it is, this WordPress glitch raises some general questions. First, is it really a bug? Assume, by a wild stretch of imagination, that a jury had to resolve this question; it could easily find an expert to answer positively, by stating that the behavior does not satisfy reasonable user expectations, and another who notes that it is not buggy behavior since it does not appear to violate any expressly stated property of the specification. (At least I did not find in the WordPress documentation any mention of either the display size of tags or a limit on tag length; if I missed it please indicate the reference.)

Is it a serious matter? Not in this particular example; uncomely Web display does not kill.   But the distinction between “small matter of esthetics” and software fault can be tenuous. We may note in particular that the possibility for large data to overflow its assigned area is a fundamental source of security risks; and even pure user interface issues can become life-threatening in the case of critical applications such as air-traffic control.

Our second putative expert is right, however: no behavior is buggy unless it contradicts a specification. Where will the spec be in such an example? There are three possibilities, each with its limitations.

The first solution is to expect that in a carefully developed system every such property will have to be specified. This is conceivable, but hard, and the question arises of how to make sure nothing has been forgotten. Past  some threshold of criticality and effort, the only specifications that count are formal; there is not that much literature on specifying user interfaces formally, since much of the work on formal specifications has understandably concentrated on issues thought to be more critical.

Because of the tediousness of specifying such general properties again and again for each case, it might be better  — this is the second solution — to specify them globally, for an entire system, or for the user interfaces of an entire class of systems. Like any serious effort at specification, if it is worth doing, it is worth doing formally.

In either of these approaches the question remains of how we know we have specified everything of interest. This question, specification completeness, is not as hopeless as most people think; I plan to write an entry about it sometime (hint:  bing for “guttag horning”). Still, it is hard to be sure you did not miss anything relevant. Remember this the next time formal methods advocates — who should know better — tell you that with their techniques there “no longer is a need to test”, or when you read about the latest OS kernel that is “guaranteed correct and secure”. However important formal methods and proofs are, they can only guarantee satisfaction of the properties that the specifier has considered and stated. To paraphrase someone [1], I would venture that

Proofs can only show the absence of envisaged bugs, never rule out the presence of unimagined ones.

This is one of the reasons why tests will always, regardless of the progress of proofs, remain an indispensable part of the software development landscape [2]. Whatever you have specified and proved, you will still want to run the system (or, for certain classes of embedded software, some simulation of it) and see the results for yourself.

What then if we do not explicitly specify the desired property (as we did in the two approaches considered so far) but testing or actual operation still reveals some behavior that is clearly unsatisfactory? On what basis do we complain to the software’s producer? A solution here, the third in our list, might be to rely on generally accepted standards of professional development. This is common in other kinds of engineering: if you commission someone to build you a house, the contract may not explicitly state that the roof should not fall on your head while you are asleep; when this happens, you will still sue and accuse the builder of malpractice. Such remedies can work for software too, but the rules are murkier because we have not accepted, or even just codified, a set of general professional practices that would cover such details as “no display of information should overflow its assigned area”.

Until then I will remember to use one short tag a lot.

References

[1] Edsger W. Dijksra, Notes on Structured Programming, in Dahl, Dijkstra, Hoare, Structured Programming, Academic Press, 1972.

[2]  See Tests And Proofs (TAP) conference series, since 2007. The next conference, program-chaired by Angelo Gargantini and Gordon Fraser, will take place in the week of the TOOLS Federated Conferences in Málaga, Spain, in the week of June 28, 2010.

The great programming haiku competition

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In a few weeks I will be teaching again my Introductory Programming course at ETH, based for the first time on the published “Touch of Class” textbook [1]. For fun (mine if no one else’s) every lecture will conclude with a haiku summarizing the topic.

I made up a few, given below, and am opening a competition for more. Every proposal should be submitted in the form of a comment to this post. Every winner’s haiku and name will appear in the course slides, and in the special Programming Haiku page which will be added to the book’s site. There are four rules:

  • The contribution has to be a proper haiku: “three unrhymed lines of five, seven, and five syllables”.
  • It must summarize the principal concept of a chapter or main section of the textbook or, better yet, of one of the course’s lectures; see [2] for the lecture plan.
  • It must give the book reference (chapter or section) or lecture number or both.
  • The prize committee’s members are secret and its judgments final.

Here, for a start, are my own examples.

Proof of the undecidability of the halting problem

Section 7.5 of the book; lecture 5.2.

If it stops, it loops,
Yet if it looped, it would stop.
Sad contradiction.

Recursion

Chapter 14, especially section 14.3; lecture 9.1.


Often, I call you.
But when the going gets tough,
I will call myself.

Topological sort

Chapter 15; lectures 11.1 and 11.2.


Partial to total?
With the right data structures,
O of m plus n.

Dynamic binding

Section 16.3; lecture 8.1.


O-O programmers:
How many to screw a bulb?
None whatsoever.

Deferred classes

Section 16.5; lecture 8.1.


Do not implement!
Though for a truly Zen spec
You need a contract.

References

[1] Touch of Class: An Introduction to Programming Well Using Objects and Contracts, Springer Verlag, 2009. See Amazon page (still wrongly says the book is not yet published).

[2] “Introduction to Programming” course at ETH Zurich, Fall 2009: course page. This does not have the slides yet, but you can see last year’s slides in last year’s page.

Rejection letter classic

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Part of the experience of being a scientist, in the industrial age of publication, is the rejection letter; especially the damning review whose author, anonymous of course, does not appear particularly competent. I have my own treasured collection, which I will publish one day. For a fiction so artfully designed as to be almost as good as the real thing, you can check  Simone Santini’s hilarious parody [1], a true classic.

Although there are a few references to it around the Web, I do not think it is as well known as it deserves to be. What Santini did was to imagine rejection letters for famous papers. He stated [2] that:

The reviews are a collage of reviews that I have seen of some papers (mine and of other people) that have been rejected because, I thought, the reviewer had completely misunderstood the paper. After a rejection at a database conference for what I thought were completely preposterous reasons, I had the idle thought that today even Codd’s paper on relational data bases (the foundation of the whole field) would never make it into a major data base conference…Many of the sentences that I use in the article are from actual reviews.

A sample from the imaginary Codd rejection letter:

E.F. CODD “A Relational Model of Data for Large Shared Data Banks.”  … The formalism is needlessly complex and mathematical, using concepts and notation with which the average data bank practitioner is unfamiliar. The paper doesn’t tell us how to translate its arcane operations into executable block access.

Adding together the lack of any real-world example, performance experiment, and implementation indication or detail, we are left with an obscure exercise using unfamiliar mathematics and of little or no practical consequence. It can be safely rejected.

All the others are gems too: Turing’s Entscheidungsproblem paper (“If the article is accepted, Turing should remember that the language of this journal is English and change the title accordingly”); Dijstra’s Goto considered harmful; Hoare’s 1969 axiomatic semantics paper (the author “should also extend the method to be applicable to a standard programming language such as COBOL or PL/I and provide the details of his implementation, possibly with a few graphics to show how the system works in practice”) etc.

To avoid a spoiler I will  cite no more;  you should read the paper if you do not know it yet. It rings so true.

References

[1] Simone Santini: We Are Sorry to Inform You …, in IEEE Computer, vol. 38, no. 12, pp. 128, 126-127, December 2005,  online on the IEEE site. There is also a copy here.

 [2] http://www.omlettesoft.com/newjournal.php3?who=Lord+Omlette&id=1134629858.

Talking about testing…

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… Let me mention the LASER summer school [1], coming up in early September, and devoted this year to “Software Testing: The Science and the Practice”. It’s in a breathtaking setting, in the wonderful Hotel del Golfo in Procchio on the island of Elba (yes, the place where Napoleon spent a little less than a year, off the coast of Tuscany)

elbahotel

and has some of the world’s top testing experts as speakers. Late registrations are still possible.

I will report here in September about some of what I learn.

Reference

 

[1] Laser summer school: the link is here.

What is the purpose of testing?

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Last year I published in IEEE Computer a short paper entitled “Seven Principles of Software Testing” [1]. Although technical, it was an opinion piece and the points were provocative enough to cause a reader, Gerald Everett, to express strong disagreement. Robert Glass, editor of the “Point/Counterpoint” rubric of the sister publication, IEEE Software, invited both of us to a debate in the form of a critique by Mr. Everett, my answer to the critique, his rejoinder to the answer, and my rejoinder to his rejoinder. The result appeared recently [2].

Other than a matter of terminology (Mr. Everett wants “testing” to cover static as well as dynamic techniques of quality assurance), the main point of disagreement was my very first principle: to test a program is to make it fail. Indeed this flies in the face of some established wisdom, which holds that testing serves to increase one’s confidence in the software; see for example the Wikipedia entry [3]. My article explains that this is a delusion and that it is more productive to limit the purpose of the testing process to what it does well, finding faults,  rather than leting it claim goals of quality assurance that are beyond its scope. Finding faults is no minor feat already. In this view — the practical view, for example as seen by a software project manager  — Dijkstra’s famous dismissal of testing (it can prove the presence of bugs, never their absence) is the greatest compliment to testing, and the most powerful advertisement one can think of for taking testing seriously.

What do you think? What is testing good for?

I should add that in terms of research this debate is a bit of a sideline.  The real goal of our work is to build completely automatic testing tools. An article on this topic will appear in the next issue of Computer (September); I will post a link to it when the issue is out.

References

[1] Bertrand Meyer: Seven Principles of Software Testing, in IEEE Computer, vol. 41, no. 8, pages 99-101, Aug. 2008; available on the IEEE site, and also in draft form here. (An earlier version, without the beautiful picture of bees and flies in the bottle drawn by Computer‘s artist, appeared as an EiffelWorld column.)

[2] Gerald D. Everett and Bertrand Meyer: Point/Counterpoint, in IEEE Software, Vol. 26, No. 4, pages 62-64, July/August 2009.  Available on the IEEE site and also here.

[3] Wikipedia entry on Software Testing.

[4] Bertrand Meyer, Ilinca Ciupa, Andreas Leitner, Arno Fiva, Emmanuel Stapf and Yi Wei: Programs that test themselves, to appear in IEEE Computer, September 2009.

“Touch of Class” published

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My textbook Touch of Class: An Introduction to Programming Well Using Objects and Contracts [1] is now available from Springer Verlag [2]. I have been told of many bookstores in Europe that have it by now; for example Amazon Germany [3] offers immediate delivery. Amazon US still lists the book as not yet published [4], but I think this will be corrected very soon.

touch_of_class

The book results from six years of teaching introductory programming at ETH Zurich. It is richly illustrated in full color (not only with technical illustrations but with numerous photographs of people and artefacts). It is pretty big, but designed so that a typical one-semester introductory course can cover most of the material.

Many topics are addressed (see table of contents below), including quite a few that are seldom seen at the introductory level. Some examples, listed here in random order: a fairly extensive introduction to software engineering including things like requirements engineering (not usually mentioned in programming courses, with results for everyone to see!) and CMMI, a detailed discussion of how to implement recursion, polymorphism and dynamic binding and their role for software architecture, multiple inheritance, lambda calculus (at an introductory level of course), a detailed analysis of the Observer and Visitor patterns, event-driven programming, the lure and dangers of references and aliasing, topological sort as an example of both algorithm and API design, high-level function closures, software tools, properties of computer hardware relevant for programmers, undecidability etc.

The progression uses an object-oriented approach throughout; the examples are in Eiffel, and four appendices present the details of Java, C#, C++ and C. Concepts of Design by Contract and rigorous development are central to the approach; for example, loops are presented as a technique for computing a result by successive approximation, with a central role for the concept of loop invariant. This is not a “formal methods” book in the sense of inflicting on the students a heavy mathematical apparatus, but it uses preconditions, postconditions and invariants throughout to alert them to the importance of reasoning rigorously about programs. The discussion introduces many principles of sound design, in line with the book’s subtitle, “Learning to Program Well”.

The general approach is “Outside-In” (also known as “Inverted Curriculum” and described at some length in some of my articles, see e.g. [5]): students have, right from the start, the possibility of working with real software, a large (150,000-line) library that has been designed specifically for that purpose. Called Traffic, this library simulates traffic in a city; it is graphical and of good enough visual quality to be attractive to today’s “Wii generation” students, something that traditional beginners’ exercises, like computing the 7-th Fibonacci number, cannot do (although we have these too as well). Using the Traffic software through its API, students can right from the first couple of weeks produce powerful applications, without understanding the internals of the library. But they do not stop there: since the whole thing is available in open source, students learn little by little how the software is made internally. Hence the name “Outside-In”: understand the interface first, then dig into the internals. Two advantages of the approach are particularly worth noting:

  • It emphasizes the value of abstraction, and particular contracts, not by preaching but by showing to students that abstraction helps them master a large body of professional-level software, doing things that would otherwise be unthinkable at an introductory level.
  • It addresses what is probably today the biggest obstacle to teaching introductory programming: the wide diversity of initial student backgrounds. The risk with traditional approaches is either to fly too high and lose the novices, or stay too low and bore those who already have programming experience. With the Outside-In method the novices can follow the exact path charted from them, from external API to internal implementation; those who already know something about programming can move ahead of the lectures and start digging into the code by themselves for information and inspiration.

(We have pretty amazing data on students’ prior programming knowledge, as  we have been surveying students for the past six years, initially at ETH and more recently at the University of York in England thanks to our colleague Manuel Oriol; some day I will post a blog entry about this specific topic.)

The book has been field-tested in its successive drafts since 2003 at ETH, for the Introduction to Programming course (which starts again in a few weeks, for the first time with the benefit of the full text in printed form). Our material, such as a full set of slides, plus exercises, video recordings of the lectures etc. is available to any instructor selecting the text. I must say that Springer did an outstanding job with the quality of the printing and I hope that instructors, students, and even some practitioners already in industry will like both form and content.

Table of contents

Front matter: Community resource, Dedication (to Tony Hoare and Niklaus Wirth), Prefaces, Student_preface, Instructor_preface, Note to instructors: what to cover?, Contents

PART I: Basics
1 The industry of pure ideas
2 Dealing with objects
3 Program structure basics
4 The interface of a class
5 Just Enough Logic
6 Creating objects and executing systems
7 Control structures
8 Routines, functional abstraction and information hiding
9 Variables, assignment and references
PART II: How things work
10 Just enough hardware
11 Describing syntax
12 Programming languages and tools
PART III: Algorithms and data structures
13 Fundamental data structures, genericity, and algorithm complexity
14 Recursion and trees
15 Devising and engineering an algorithm: Topological Sort
PART IV: Object-Oriented Techniques
16 Inheritance
17 Operations as objects: agents and lambda calculus
18 Event-driven design
PART V: Towards software engineering
19 Introduction to software engineering
PART VI: Appendices
A An introduction to Java (from material by Marco Piccioni)
B An introduction to C# (from material by Benjamin Morandi)
C An introduction to C++ (from material by Nadia Polikarpova)
D From C++ to C
E Using the EiffelStudio environment
Picture credits
Index

References

[1] Bertrand Meyer, Touch of Class: An Introduction to Programming Well Using Objects and Contracts, Springer Verlag, 2009, 876+lxiv pages, Hardcover, ISBN: 978-3-540-92144-8.

[2] Publisher page for [1]: see  here. List price: $79.95. (The page says “Ships in 3 to 4 weeks” but I think this is incorrect as the book is available; I’ll try to get the mention corrected.)

[3] Amazon.de page: see here. List price: EUR 53.45 (with offers starting at EUR 41.67).

[4] Amazon.com page: see here. List price: $63.96.

[5] Michela Pedroni and Bertrand Meyer: The Inverted Curriculum in Practice, in Proceedings of SIGCSE 2006, ACM, Houston (Texas), 1-5 March 2006, pages 481-485; available online.

One cheer for incremental research

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[Note: an updated version of this article (June 2011) appears in the Communications of the ACM blog.]

The world of research funding, always a little strange, has of late been prey to a new craze: paradigm-shift mania. We will only fund twenty curly-haired cranky-sounding visionaries in the hope that one of them will invent relativity. The rest of you — bit-players! Petty functionaries! Slaves toiling at incremental research!  — should be ashamed of even asking.

Take this from the US National Science Foundation’s current description of funding for Computer Systems Research [1]:

CSR-funded projects will enable significant progress on challenging high-impact problems, as opposed to incremental progress on familiar problems.

 The European Research Council is not to be left behind [2]:

Projects being highly ambitious, pioneering and unconventional

Research proposed for funding to the ERC should aim high, both with regard to the ambition of the envisaged scientific achievements as well as to the creativity and originality of proposed approaches, including unconventional methodologies and investigations at the interface between established disciplines. Proposals should rise to pioneering and far-reaching challenges at the frontiers of the field(s) addressed, and involve new, ground-breaking or unconventional methodologies, whose risky outlook is justified by the possibility of a major breakthrough with an impact beyond a specific research domain/discipline.

Frontiers! Breakthrough! Rise! Aim high! Creativity! Risk! Impact! Pass me the adjective bottle. Ground-breaking! Unconventional! Highly ambitious! Major! Far-reaching! Pioneering! Galileo and Pasteur only please — others need not apply.

As everyone knows including the people who write such calls, this is balderdash. First, 99.97% of all research (precise statistic derived from my own ground-breaking research, further funding welcome) is incremental. Second, when a “breakthrough” does happen — the remaining 0.03%  — it was often not planned as a breakthrough.

Incremental research is a most glorious (I have my own supply of adjectives) mode of doing science. Beginning PhD students can be forgiven for believing the myth of the lone genius who penetrates the secrets of time and space by thinking aloud during long walks with his best friend [3]; we all, at some stage, shared that delightful delusion. But every researcher, presumably including those who go on to lead research agencies,  quickly grows up and learns that it is not how things happen. You read someone else’s solution to a problem, and you improve on it. Any history of science will tell you that for every teenager who from getting hit by a falling apple intuits the structure of the universe there are hundreds of great researchers who look at the state of the art and decide they can do a trifle better.

Here is a still recent example, particularly telling because we have the account from the scientist himself. It would not be much of an exaggeration to characterize the entire field of program proving over the past four decades as a long series of variations on Tony Hoare’s 1969 Axiomatic Semantics paper [4]. Here Hoare’s recollection, from his Turing Award lecture [5]:

In October 1968, as I unpacked my papers in my new home in Belfast, I came across an obscure preprint of an article by Bob Floyd entitled “Assigning Meanings to Programs.” What a stroke of luck! At last I could see a way to achieve my hopes for my research. Thus I wrote my first paper on the axiomatic approach to computer programming, published in the Communications of the ACM in October 1969.

(See also note [6].) Had the research been submitted for funding, we can imagine the reaction: “Dear Sir, as you yourself admit, Floyd has had the basic idea [7] and you are just trying to present the result better. This is incremental research; we are in the paradigm-shift business.” And yet if Floyd had the core concepts right it is Hoare’s paper that reworked and extended them into a form that makes practical semantic specifications and proofs possible. Incremental research at its best.

The people in charge of research programs at the NSF and ERC are themselves scientists and know all this. How come they publish such absurd pronouncements? There are two reasons. One is the typical academic’s fascination with industry and its models. Having heard that venture capitalists routinely fund ten projects and expect one to succeed, they want to transpose that model to science funding; hence the emphasis on “risk”. But the transposition is doubtful because venture capitalists assess their wards all the time and, as soon as they decide a venture is not going to break out, they cut the funding overnight, often causing the company to go under. This does not happen in the world of science: most projects, and certainly any project that is supposed to break new ground, gets funded for a minimum of three to five years. If the project peters out, the purse-holder will only realize it after spending all the money.

The second reason is a sincere desire to avoid mediocrity. Here we can sympathize with the funding executives: they have seen too many “here is my epsilon addition to the latest buzzword” proposals. The last time I was at ECOOP, in 2005, it seemed every paper was about bringing some little twist to aspect-oriented programming. This kind of research benefits no one and it is understandable that the research funders want people to innovate. But telling submitters that every project has to be epochal (surprisingly, “epochal” is missing from the adjectives in the descriptions above  — I am sure this will soon be corrected) will not achieve this result.

It achieves something else, good neither for research nor for research funding: promise inflation. Being told that they have to be Darwin or nothing, researchers learn the game and promise the moon; they also get the part about “risk” and emphasize how uncertain the whole thing is and how high the likelihood it will fail. (Indeed, since — if it works — it will let cars run from water seamlessly extracted from the ambient air, and with the excedent produce free afternoon tea.)

By itself this is mostly entertainment, as no one believes the hyped promises. The real harm, however, is to honest scientists who work in the normal way, proposing to bring an important contribution to the  solution of an important problem. They risk being dismissed as small-timers with no vision.

Some funding agencies have kept their heads cool. How refreshing, after the above quotes, to read the general description of funding by the Swiss National Science Foundation [8]:

The central criteria for evaluation are the scientific quality, originality and project methodology as well as qualifications and track record of the applicants. Grants are awarded on a competitive basis.

In a few words, it says all there is to say. Quality, originality, methodology, and track record. Will the research be “ground-breaking” or “incremental”? We will know when it is done.

I am convinced that the other agencies will come to their senses and stop the paradigm-shift nonsense. One reason for hope is in the very excesses of the currently fashionable style. The European Research Council quote includes, by my count, nineteen ways of saying that proposals must be daring. Now it is a pretty universal rule of life that someone who finds it necessary to say the same thing nineteen times in a single paragraph does not feel sure about it. He is trying to convince himself. At some point the people in charge will realize that such hype does not breed breakthroughs; it breeds more hype.

Until that happens there is something that some of us can do: refuse to play the game. Of course we are all convinced that our latest idea is the most important one ever conceived by humankind, and we want to picture it in the most favorable light. But we should resist the promise inflation. Such honesty comes at a risk. (I still remember a project proposal, many years ago, which came back with glowing reviews: the topic was important, the ideas right, the team competent. The agency officer’s verdict: reject. The proposers are certain to succeed, so it’s not research.) For some people, there is really no choice but to follow the lead: if your entire career depends on getting external funding, no amount of exhortation will prevent you from saying what the purse-holders want to hear. But those of us who do have a choice (that is to say, will survive even if a project is rejected) should refuse the compromission. We should present our research ideas for what they are.

So: one cheer for incremental research.

Wait, isn’t the phrase supposed to be “two cheers” [9]?

All right, but let’s go at it incrementally. One and one-tenth cheer for incremental research. 

References

 

[1]  National Science Foundation, Division of Computer and Network Systems: Computer Systems Research  (CSR), at http://www.nsf.gov/funding/pgm_summ.jsp?pims_id=13385.

[2] European Research Council: Advanced Investigators Grant, at http://erc.europa.eu/index.cfmfuseaction=page.display&topicID=66.

[3] The Berne years; see any biography of Albert Einstein.

[4] C.A.R. Hoare: An axiomatic basis for computer programming, in Communications of the ACM, vol. 12, no 10, pages 576–580,583, October 1969.

[5] C.A.R. Hoare: The Emperor’s Old Clothes, in Communications of the ACM, vol. 24, no.  2, pages 75 – 83, February 1981.

[6] In the first version of this essay I wrote “Someone should celebrate the anniversary!”. Moshe Vardi, editor of Communications of the ACM, has informed me that the October 2009 issue will include a retrospective by Hoare on the 1969 paper. I cannot wait to see it.

[7] Robert W. Floyd: Assigning meanings to programs, in Proceedings of the American Mathematical Society Symposia on Applied Mathematics, vol. 19, pp. 19–31, 1967.

[8] Swiss National Science Foundation:  Projects – Investigator-Driven Research, at http://www.snf.ch/E/funding/projects/Pages/default.aspx. Disclosure: The SNSF kindly funds some of my research.

[9] E.M. Forster: Two Cheers for Democracy, Edward Arnold, 1951.