Archive for the ‘General technology’ Category.

Guest article: funding great research

In a blog article posted in its original version on this blog [1] and in a revised version on the Communications of the ACM blog [2], I emphasized the relevance of incremental research. Recently Mikkel Thorup sent me some interesting comments, which I am publishing here as the first Guest Column of this blog.

References

[1] Bertrand Meyer: One Cheer for Incremental Research, in the present blog, 10 August 2009, available here

[2] Bertrand Meyer: Long Live Incremental Research, in Communications of the ACM Blog, 13 June 2011, available here.

Guest article by Mikkel Thorup: Funding Great Research

Research foundations want great research projects. However, a while back Bertrand Meyer wrote an interesting blog post: Long Live Incremental Research [2]. With examples he showed that many of the greatest results of research could not possibly be the projected results of great sounding project descriptions. His conclusion is that we should drop the high-flying ambitions from project descriptions, and instead support more incremental research proposals, hoping that great stuff will happen on the way. Indeed incremental research is perfect for research projects with predictable deliverables. However, I suggest the opposite conclusion; namely that we for some of the funding drop the project description.

The basic idea is that foundations should encourage researchers to look for results far better than those that can reasonably be projected. In particular, researchers should be free to follow their inspiration when they see new exiting opportunities. This is not done by tying researchers to incremental projects. Instead we can sometimes switch to result based funding, that is, funding based on results already achieved (with emphasis on the more recent past). Such result based funding is more like rewards for great results, and it offers researchers the perfect incentive to do their very best so as to secure future funding.

Consider a researcher with a history of brilliant ideas taking research in surprising new directions. If we try casting this as a project, the referees will rightly complain: “It is not clear how the applicant will come up with a brilliant idea, nor is it clear what the surprise will be”. With such lack of focus and feasibility, a low project score is expected.  If the project description has a predefined weight of, say, 40%, then the overall score will be too low for funding, regardless of the researcher’s established track record of succeeding in unlikely situations.  However, research needs great new ideas. Therefore we need some result based funding so that we can support researchers with a proven talent for generating great new ideas even if we do not quite understand how it will happen.

The above problem is often very real in my field of theoretical computer science. Like in other fields, theoretical research is only interesting if it contains surprises (otherwise it is more like development). A project plan would make sense if the starting point was a surprising idea or approach that it would take years to develop, but in theory, the most exciting ideas are often strikingly simple. When first you have such an idea, you are typically close to done, ready to start writing a paper. Thus, if you have a great idea when you apply for a grant, you will typically be done long before you get the grant. The essence of the research is thus the unpredictable search for powerful ideas and insights. The most appropriate project description is therefore just a description of the importance of the area to be researched and the type of results aimed for. The track record shows which researchers have the talent to succeed.

Dropping the how-part of the project description will greatly increase methodological diversity, allowing researchers to use the strategy that has proved most suitable for their area and their own talent and skills.  As a simple example, Bertrand suggested funding incremental research, hoping that great surprising things would turn up on the way. My strategy is the opposite. I try to spend as much time as possible on overly ambitious targets. Most of the time I fail, but I rarely come home empty-handed, for by studying the unknown I nearly always discover something new, sometimes even more interesting than the original target. From the perspective of ambition, I see it as an advantage that I minimize time spend on easy targets, but foundations seem to prefer that you take a planned path with some guaranteed targets on the way. The point here is not to argue whether one strategy is superior to the other, but rather to embrace the diversity of strategies that may work depending on the area and the individual researcher.

Perhaps more seriously, if a target is hard to achieve, it may be because it requires a crazy approach that would not look reasonable to anyone else, but which may work for a researcher thanks to his special talents and intuition. Indeed I have often been positively surprised seeing how others succeeded using an approach I had myself dismissed.  As a project, such crazy approaches would fail on perceived feasibility, but the point in result based funding is that researchers are free to use whatever approach they find most efficient. Funding is given to those who prove successful. This gives the perfect incentive to do great work, securing future funding.

Result-based funding would also reduce resources needed to evaluate applications. It is very hard for a general panel to evaluate the methodology and success probability of a project.  Moreover, it requires an intimate knowledge of a field to evaluate how big a difference a result would make relative to what is already known. However, handling published results, we know what happened and we can rely on peer-review for the difference it made to the field. All the panel has to do is to evaluate how the successes meet with the objectives of the foundation.

Let us, as an example, take something like the ERC Advanced Investigator Grant which welcomes high risk high gain research. It would seem that aiming for surprising breakthroughs in an important area would fall well within this scope. Having researchers with proven skills explore the area and follow their inspiration may be the optimal strategy. Uncertainty about what they would find should not be worse than high risk. In fact, based on past performance, it may be safe to assume that they will discover something interesting if not ground-breaking. However, when projects are scored on focused feasibility, such projects will fail even if their expected return is very high. It has to be possible to get a high overall score for promising research even if standard project parameters like focus and feasibility would be counterproductive.  At the end of the day, what we want are results, not project descriptions, so what should determine the overall score is which proposal is expected to yield the greatest results.

Long live great research!

Mikkel Thorup

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Averaging

 

A statistical textbook [1] contains this gem of wisdom:

Only a fool would conclude that a painting that was judged as excellent by one person and contemptible by another ought therefore to be classified as mediocre.

Common sense indeed; but does the procedure not recall how the typical conference program committee works, with averages obligingly computed by the supporting web-based systems?

Reference

[1] David C. Howell: Statistical Methods for Psychology, sixth edition, Thomson-Wadsworth, 2007

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Fun with Bayes

 

Try this:  go to translate.google.com, choose Russian as the source and English as the target languages. In the input field, type“Андрей Иванович мне писал” or, if you do not have a Cyrillic keyboard, the transliteration into the Latin alphabet, “Andrej Ivanovich mne pisal”, which (unless you uncheck the default option) will be automatically transcribed into Cyrillic as you go. The correct English translation appears: “Andrey wrote to me”.

Correct yes, but partial: the input did not read “Andrey” but “Andrey Ivanovich”. Russians have a first name, a last name and also a “patronymic”  based on the father’s name: our Andrey’s father is or was called Ivan. Following the characters in, say, War and Peace, would be next to impossible without patronymics (it’s hard enough with them). On the other hand, English usually omits the patronymic, so if you are translating something simpler than a Tolstoy novel it is reasonable for an automated tool to yield “Andrey” as the translation of “Andrey Ivanovich”. In some cases, depending on the context, it gives “Andrei”, and in some others the anglicized “Andrew”.

Google Translate has yet another translation for “Andrey Ivanovich”. Assume that you want to be specific; maybe you know two people called Andrey and use the patronymic to distinguish between them. You want to say, for example,

       I have in mind not Andrey Nikolaevich, but Andrey Petrovich.

You can enter this as “Ja imeju v vidu ne Andreja Nikolaevicha, no Andreja Petrovicha”, or copy-paste the Cyrillic: Я имею в виду не Андрея Николаевича, но Андрея Петровича. (Note for Russian speakers: the word expected after the comma is of course а, but Google Translate, knowing that а can mean “and” in other contexts, translates it here with the opposite of the intended meaning. This is why I use но, which sounds strange but understandable, and is correctly translated.). Try it now and see what comes out on the English side:

      I do not mean Kolmogorov, but Andrei Petrovich.

Google Translate, in other words, has another translation for “Andrey Nikolaevich”:

       Kolmogorov

The great mathematician A.N. Kolmogorov (1903-1987) indeed had this first name and this patronymic; to conclude that anyone with these names is also called Kolmogorov is not, however, a step that most of us are prepared to take, especially those of us with a (living) friend called Andrey Nikolaevich.

A favorite of Russians is “Dobroje Utro, Dmitri Anatolevich”, meaning “Good morning, Dimitri Anatolevich”, which Google translates into “Good morning, Mr. President”. I will let you figure this one out.

All the translations cited were, by the way, obtained on the date of this post; algorithms can change. For other examples, see this page, in Russian (thanks to Sergey Velder for bringing it to my attention).

What happened? Automatic translation has made great progress in recent years, largely as a result of the switch from structural, precise techniques based on linguistic theory to approximate methods based on statistics. These methods rely on an immense corpus of existing human translations, accessible on the Internet, and apply Bayesian techniques to match every text element to the most frequently encountered translation of a similar phrase in existing translations. This switch has caused a revolution in translation, making it possible to get approximate equivalents. Personally I find them most useful for a language I do not know at all: if I want to read a Web page in Korean, I can get its general idea, which I could not have done fifteen years ago without finding a native speaker. For a language that I know imperfectly, the help is less clear, because the translations are almost never entirely right; in fact they are almost always, beyond the level of simple phrases, grammatically incorrect.

With Bayesian techniques it is understandable why “Andrey Nikolaevich” sometimes comes out in English as “Kolmogorov”: he is probably the most famous of all Andrey Nikolaevichs in Google’s database of Russian-English translation pairs. If you do not know the database, the behavior is mysterious, as you cannot usually guess whether the translation in a particular context will be “Andrey, “Andrei”, “Andrew”, “Andrey N.” or “Kolmogorov”, the five variants that I have seen (try your own experiments!). Some cases are predictable once you know that the techniques are statistical: if you include the word “Teorema” (theorem) anywhere close, you are sure to get “Kolmogorov”. But usually there is no obvious clue.

Statistical techniques are great but such examples, beyond the fun, show their limits. I truly hope that in the future they can be combined with more exact techniques based on sound linguistics.

Postscript: are you bytypal?

Perhaps I should explain why I use Google Translate with Russian as the source language. I do not use it for translation, but I do need it to type texts in Russian. I could use a Cyrillic keyboard, but I don’t because I am a very fast touch typist on the English (QWERTY) keyboard. (Learning to type at a professional level early in life was one of the most useful skills I ever acquired — not as useful as grammar, set theory or axiomatic semantics, but far more useful than separation logic.) So it is convenient for me to type in Latin letters, say “Dostojeksky”, and rely on a tool that immediately transliterates into the Cyrillic equivalent, here Достоевский . Then I can copy-paste the result into, for example, an email to a Russian-speaking recipient.

I used to rely on a tool that does exactly this, Translit (www.translit.ru); I have of late found Google Translate generally more convenient because it does not just transliterate but relies on its database to correct some typos. I do not need the translation (except possibly to check that what I wrote makes sense), but I see it anyway; that is how I ran into the Bayesian fun described above.

As a matter of fact the transliteration tool is good but, as often with software from Google, only  “almost” right. Sometimes it simply refuses to transliterate what I wrote, because it insists on its own misguided idea of what I meant. The Auto Correct option of Microsoft Office has the good sense, when it wrongly corrects your input and  you retype it, to obey you the second time around; but Google Translate’s transliteration facility does not seem to have any such policy: it sticks to its own view, right or wrong. As a consequence it has occasionally taken me a good five minutes of fighting the tool to enter a single word. Such glitches might be removed over time, but at the moment they are sufficiently annoying that I am thinking of teaching myself to touch-type in Cyrillic.

Is this possible? Initially I learned to type on the French (AZERTY) keyboard and I had to unlearn it, since otherwise a Q would occasionally come out as an A, a Z as a W and a semicolon as an M. I know bilingual people, but none who have programmed themselves to touch-type on different keyboards. Anyone out there willing to comment on the experience of bytypalism?

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Nastiness in computer science

 

Recycled(This article was originally published in the CACM blog.)
 

Are we malevolent grumps? Nothing personal, but as a community computer scientists sometimes seem to succumb to negativism.

They admit it themselves. A common complaint in the profession (at least in academia) is that instead of taking a cue from our colleagues in more cogently organized fields such as physics, who band together for funds, promotion, and recognition, we are incurably fractious. In committees, for example, we damage everyone’s chances by badmouthing colleagues with approaches other than ours. At least this is a widely perceived view (“Circling the wagons and shooting inward,” as Greg Andrews put it in a recent discussion). Is it accurate?

One statistic that I have heard cited is that in 1-to-5 evaluations of projects submitted to the U.S. National Science Foundation the average grade of computer science projects is one full point lower than the average for other disciplines. This is secondhand information, however, and I would be interested to know if readers with direct knowledge of the situation can confirm or disprove it.

More such examples can be found in the material from a recent keynote by Jeffrey Naughton, full of fascinating insights (see his Powerpoint slides External Link). Naughton, a database expert, mentions that only one paper out of 350 submissions to SIGMOD 2010 received a unanimous “accept” from its referees, and only four had an average accept recommendation. As he writes, “either we all suck or something is broken!

Much of the other evidence I have seen and heard is anecdotal, but persistent enough to make one wonder if there is something special with us. I am reminded of a committee for a generously funded CS award some time ago, where we came close to not giving the prize at all because we only had “good” proposals, and none that a committee member was willing to die for. The committee did come to its senses, and afterwards several members wondered aloud what was the reason for this perfectionism that almost made us waste a great opportunity to reward successful initiatives and promote the discipline.

We come across such cases so often—the research project review that gratuitously but lethally states that you have “less than a 10% chance” of reaching your goals, the killer argument  “I didn’t hear anything that surprised me” after a candidate’s talk—that we consider such nastiness normal without asking any more whether it is ethical or helpful. (The “surprise” comment is particularly vicious. Its real purpose is to make its author look smart and knowledgeable about the ways of the world, since he is so hard to surprise; and few people are ready to contradict it: Who wants to admit that he is naïve enough to have been surprised?)

A particular source of evidence is refereeing, as in the SIGMOD example.  I keep wondering at the sheer nastiness of referees in CS venues.

We should note that the large number of rejected submissions is not by itself the problem. Naughton complains that researchers spend their entire careers being graded, as if passing exams again and again. Well, I too like acceptance better than rejection, but we have to consider the reality: with acceptance rates in the 8%-20% range at good conferences, much refereeing is bound to be negative. Nor can we angelically hope for higher acceptance rates overall; research is a competitive business, and we are evaluated at every step of our careers, whether we like it or not. One could argue that most papers submitted to ICSE and ESEC are pretty reasonable contributions to software engineering, and hence that these conferences should accept four out of five submissions; but the only practical consequence would be that some other venue would soon replace ICSE and ESEC as the publication place that matters in software engineering. In reality, rejection remains a frequent occurrence even for established authors.

Rejecting a paper, however, is not the same thing as insulting the author under the convenient cover of anonymity.

The particular combination of incompetence and arrogance that characterizes much of what Naughton calls “bad refereeing” always stings when you are on the receiving end, although after a while it can be retrospectively funny; one day I will publish some of my own inventory, collected over the years. As a preview, here are two comments on the first paper I wrote on Eiffel, rejected in 1987 by the IEEE Transactions on Software Engineering (it was later published, thanks to a more enlightened editor, Robert Glass, in the Journal of Systems and Software, 8, 1988, pp. 199-246 External Link). The IEEE rejection was on the basis of such review gems as:

  • I think time will show that inheritance (section 1.5.3) is a terrible idea.
  • Systems that do automatic garbage collection and prevent the designer from doing his own memory management are not good systems for industrial-strength software engineering.

One of the reviewers also wrote: “But of course, the bulk of the paper is contained in Part 2, where we are given code fragments showing how well things can be done in Eiffel. I only read 2.1 arrays. After that I could not bring myself to waste the time to read the others.” This is sheer boorishness passing itself off as refereeing. I wonder if editors in other, more established disciplines tolerate such attitudes. I also have the impression that in non-CS journals the editor has more personal leverage. How can the editor of IEEE-TSE have based his decision on such a biased an unprofessional review? Quis custodiet ipsoes custodes?

“More established disciplines”: Indeed, the usual excuse is that we are still a young field, suffering from adolescent aggressiveness. If so, it may be, as Lance Fortnow has argued in a more general context, “time for computer science to grow up.” After some 60 or 70 years we are not so young any more.

What is your experience? Is the grass greener elsewhere? Are we just like everyone else, or do we truly have a nastiness problem in computer science?

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A safe and stable solution

Reading about the latest hullabaloo around Android’s usage of Java, and more generally following the incessant flow of news about X suing Y in the software industry (with many combinations of X and Y) over Java and other object-oriented technologies, someone with an Eiffel perspective can only smile. Throughout its history, suggestions to use Eiffel have often been met initially — along with “Will Eiffel still be around next year?”, becoming truly riotous after 25 years — with objections of proprietariness, apparently because Eiffel initially came from a startup company. In contrast, many other approaches, from C++ to Smalltalk and Java, somehow managed to get favorable vibes from the media; the respective institutions, from AT&T to Xerox and Sun, must be disinterested benefactors of humanity.

Now many who believed this are experiencing a next-morning surprise, discovering under daylight that the person next to whom they wake up is covered with patents and lawsuits.

For their part, people who adopted Eiffel over the years and went on to develop project after project  do not have to stay awake worrying about legal issues and the effects of corporate takeovers; they can instead devote their time to building the best software possible with adequate methods, notations and tools.

This is a good time to recall the regulatory situation of Eiffel. First, the Eiffel Software implementation (EiffelStudio): the product can be used through either an open-source and a proprietary licenses. With both licenses the software is exactly the same; what differs is the status of the code users generate: with the open-source license, they are requested to make their own programs open-source; to keep their code proprietary, they need the commercial license. This is a fair and symmetric requirement. It is made even more attractive by the absence of any run-time fees or royalties of the kind typically charged by database vendors.

The open-source availability of the entire environment, over 2.5 millions line of (mostly Eiffel) code, has spurred the development of countless community contributions, with many more in progress.

Now for the general picture on the language, separate from any particular implementation. Java’s evolution has always been tightly controlled by Sun and now its successor Oracle. There may actually be technical arguments in favor of the designers retaining a strong say in the evolution of a language, but they no longer seem to apply any more now that most of the Java creators have left the company. Contrast this with Eiffel, which is entirely under the control of an international standards committee at ECMA International, the oldest and arguably the most prestigious international standards body for information technology. The standard is freely available online from the ECMA site [1]. It is also an ISO standard [2].

The standardization process is the usual ECMA setup, enabling any interested party to participate. This is not just a statement of principle but the reality, to which I can personally testify since, in spite of being the language’s original designer and author of the reference book, I lost countless battles in the discussions that led to the current standard and continue in preparation of the next version. While I was not always pleased on the moment, the committee’s collegial approach has led to a much more solid result than any single person could have achieved.

The work of ECMA TC49-TG4 (the Eiffel standard committee) has disproved the conventional view that committees can only design camels. In fact TC49-TG4 has constantly worked to keep the language simple and manageable, not hesitating to remove features deemed obsolete or problematic, while extending the range of the language and increasing the Eiffel programmer’s power of expression. As a result, Eiffel today is an immensely better language than when we started our work in 2002. Without a strong community-based process we would never, for example, have made Eiffel the first widespread language to guarantee void-safety (the compile-time removal of null-pointer-dereferencing errors), a breakthrough for software reliability.

Open, fair, free from lawsuits and commercial fights, supported by an enthusiastic community: for projects that need a modern quality-focused software framework, Eiffel is a safe and stable solution.

References

[1] ECMA International: Standard ECMA-367: Eiffel: Analysis, Design and Programming Language, 2nd edition (June 2006), available here (free download).

[2] International Organization for Standardization: ISO/IEC 25436:2006: Information technology — Eiffel: Analysis, Design and Programming Language, available here (for a fee; same text as [1], different formatting).

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Stendhal on abstraction

This week we step away from our usual sources of quotations — the Hoares and Dijkstras and Knuths — in favor an author who might seem like an unlikely inspiration for a technology blog: Stendhal. A scientist may like anyone else be fascinated by Balzac, Flaubert, Tolstoy or Dostoevsky, but they live in an entirely different realm; Stendhal is the mathematician’s novelist. Not particularly through the themes of his works (as could be the case with  Borges or Eco), but because of their clear structure and elegant style,  impeccable in its conciseness and razor-like in its precision. Undoubtedly his writing was shaped by his initial education; he prepared for the entrance exam of the then very young École Polytechnique, although at the last moment he yielded instead to the call of the clarion.

The scientific way of thinking was not just an influence on his writing; he understood the principles of scientific reasoning and knew how to explain them. Witness the following text, which explains just about as well as anything I know the importance of abstraction. In software engineering (see for example [1]), abstraction is the key talent, a talent of a paradoxical nature: the basic ideas take a few minutes to explain, and a lifetime to master. In this effort, going back to the childhood memories of Henri Beyle (Stendhal’s real name) is not a bad start.

Stendhal’s Life of Henri Brulard is an autobiography, with only the thinnest of disguises into a novel (compare the hero’s name with the author’s). In telling the story of his morose childhood in Grenoble, the narrator grumbles about the incompetence of his first mathematics teacher, a Mr. Dupuy, who taught mathematics “as a set of recipes to make vinegar” (comme une suite de recettes pour faire du vinaigre) and tells how his father found a slightly better one, Mr. Chabert. Here is the rest of the story, already cited in [2]. The translation is mine; you can read the original below, as well as a German version. Instead of stacks and circles  — or a university’s commencement day, see last week’s posting — the examples invoke eggs and cheese, but wouldn’t you agree that this paragraph is as good a definition of abstraction, directly applicable to software abstractions, and specifically to abstract data types and object abstractions (yes, it does discuss “objects”!), as any other?

So I went to see Mr. Chabert. Mr. Chabert was indeed less ignorant than Mr. Dupuy. Through him I discovered Euler and his problems on the number of eggs that a peasant woman brings to the market where a scoundrel steals a fifth of them, then she leaves behind the entire half of the remainder and so forth. This opened my mind, I glimpsed what it means to use the tool called algebra. I’ll be damned if anyone had ever explained it to me; endlessly Mr. Dupuy spun pompous sentences on the topic, but never did he say this one simple thing: it is a division of labor, and like every division of labor it creates wonders by allowing the mind to concentrate all its forces on just one side of objects, on just one of their qualities. What difference it would have made if Mr. Dupuy had told us: This cheese is soft or is it hard; it is white, it is blue; it is old, it is young; it is mine, it is yours; it is light or it is heavy. Of so many qualities, let us only consider the weight. Whatever that weight is, let us call it A. And now, no longer thinking of cheese, let us apply to A everything we know about quantities. Such a simple thing; and yet no one was explaining it to us in that far-away province [3]. Since that time, however, the influence of the École Polytechnique and Lagrange’s ideas may have trickled down to the provinces.

References

[1] Jeff Kramer: Is abstraction the key to computing?, in Communications of The ACM, vol. 50, 2007, pages 36-42.
[2] Bertrand Meyer and Claude Baudoin: Méthodes de Programmation, Eyrolles, 1978, third edition, 1982.
[3] No doubt readers from Grenoble, site of great universities and specifically one of the shrines of French computer science, will appreciate how Stendhal calls it  “that backwater” (cette province reculée).

Original French text

J’allai donc chez M. Chabert. M. Chabert était dans le fait moins ignare que M. Dupuy. Je trouvai chez lui Euler et ses problèmes sur le nombre d’œufs qu’une paysanne apportait au marché lorsqu’un méchant lui en vole un cinquième, puis elle laisse toute la moitié du reste, etc., etc. Cela m’ouvrit l’esprit, j’entrevis ce que c’était que se servir de l’instrument nommé algèbre. Du diable si personne me l’avait jamais dit ; sans cesse M. Dupuy faisait des phrases emphatiques sur ce sujet, mais jamais ce mot simple : c’est une division du travail qui produit des prodiges comme toutes les divisions du travail et permet à l’esprit de réunir toutes ses forces sur un seul côté des objets, sur une seule de leurs qualités. Quelle différence pour nous si M. Dupuy nous eût dit : Ce fromage est mou ou il est dur ; il est blanc, il est bleu ; il est vieux, il est jeune ; il est à moi, il est à toi ; il est léger ou il est lourd. De tant de qualités ne considérons absolument que le poids. Quel que soit ce poids, appelons-le A. Maintenant, sans plus penser absolument au fromage, appliquons à A tout ce que nous savons des quantités. Cette chose si simple, personne ne nous la disait dans cette province reculée ; depuis cette époque, l’École polytechnique et les idées de Lagrange auront reflué vers la province.

German translation (by Benjamin Morandi)

Deshalb ging ich zu Herrn Chabert. In der Tat war Herr Chabert weniger ignorant als Herr Dupuy. Bei ihm fand ich Euler und seine Probleme über die Zahl von Eiern, die eine Bäuerin zum Markt brachte, als ein Schurke ihr ein Fünftel stahl, sie dann die Hälfte des Restes hinterliest u.s.w. Es hat mir die Augen geöffnet. Ich sah was es bedeutet, das Algebra genannte Werkzeug zu benutzen. Unaufhörlich machte Herr Dupuy emphatische Sätze über dieses Thema, aber niemals dieses einfache Wort: Es ist eine Arbeitsteilung, die wie alle Arbeitsteilungen Wunder herstellt und dem Geist ermöglicht seine Kraft ganz auf eine einzige Seite von Objekten zu konzentrieren, auf eine Einzige ihrer Qualitäten. Welch Unterschied für uns, wenn uns Herr Dupuy gesagt hätte: Dieser Käse ist weich oder er ist hart; er ist weiss, er ist blau; er ist alt, er ist jung; er gehört dir, er gehört mir; er ist leicht oder er ist schwer. Bei so vielen Qualitäten betrachten wir unbedingt nur das Gewicht. Was dieses Gewicht auch sei, nennen wir es A. Jetzt, ohne unbedingt weiterhin an Käse denken zu wollen, wenden wir auf A alles an, was wir über Mengen wissen. Diese einfach Sache sagte uns niemand in dieser zurückgezogenen Provinz; von dieser Epoche an werden die École Polytechnique und die Ideen von Lagrange in die Provinz zurückgeflossen sein.

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From duplication to duplicity: a short history of the technology of knowledge transfer

1440:

Gutenberg discovers the secret of producing, out of one text, many books for the benefit of many people.

2007:

Von und Zu Guttenberg discovers the secret of producing, out of many texts, one book for the benefit of one person.

 

 

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Email and its perils

Email is fast and convenient. It is also risky. Here are three common sources of incidents with email. They are not new, but they keep biting even the most experienced email users.

1. Risks of using Bcc

Alice writes to Bob. She wants Carol to know what she wrote, but she does not want Bob to know that she is keeping Carol informed. So she copies Carol in the form of a “Blind carbon copy” (Bcc). The Bcc mechanism is meant exactly for such situations: while Bcc recipients see the list of other recipients (To and Cc), these other recipients see no mention of Bcc recipients.

Now Carol sees the messages and responds to Alice. But to respond she uses, perhaps inadvertently, “Reply all”. The reply goes to both Alice and Bob. All of Alice’s efforts to keep mum about Carol’s involvement are lost!

The risk in this situation is that Alice has no way to control what Carol may do. At issue here is not a conscious effort by Carol to break confidentiality: in that case Alice could do nothing anyway as soon as she has sent Carol the information. The worrying possibility is that Carol may use “Reply all” by mistake.

Rule 1 (temporary): never use Bcc. Alice should send the message to Bob only, and forward a copy separately to Carol alone.

I start with this form of the rule because it is easy to remember and usually appropriate, but in one case it is too strong. Remove Bob from the picture. Then if Carol is a  person, it is pointless for Alice to use Bcc for her, rather than To (or Cc), since Carol knows everything there is to know. But now assume that Carol is actually the name of a mailing list. Members of the mailing list should know the originator, Alice, but they should not know about each other, or even about the name of the list. If these are the constraints, there is no risk in using Bcc. Hence the revised version of the rule:

Rule 1 (final): never use Bcc except for all recipients of a message.

Indeed what was wrong in the first example was not the use of Bcc as such, but the mix with To (or Cc). Bcc must be used only for either all the recipients of a message or none of them.

2. Risks of not using Bcc

Very recent case (today, actually) from the refereeing process of a prestigious computer science conference. The program chair sends to all authors of submitted papers,  say <submitters@famous_computer_science_conference.org>, a general message about the refereeing process. But he uses that address in the “To” or “Cc” field, not “bcc”.

One author, say Alice, has a question about the process and responds to the message, inadvertently using “Reply all”. Then:

  • Everyone knows that Alice has submitted a paper.
  • Many of the other authors are away and have “automatic reply” set up. So Alice herself now knows the names of quite a few other members of the community who have submitted papers!

famous_computer_science_conference.org” is the most important conference in its field, and essentially every researcher in that community submits at least one paper every year. Knowing who has submitted is confidential information. That information becomes interesting a few months later, when the conference program is published and you find out that Bob tried and was not accepted. All the more juicy information, of course, if Bob is a senior (and arrogant) researcher.

Rule 2: when a mailing list includes people who should not know who else is on the list, either set up the list so that only the administrators can post to it, or use Bcc (respecting Rule 1, in its final form).

3. The importance of not being Allison

Another common risk, which strikes all the time, is automatic address completion by email clients (Outlook, Thunderbird etc.). You type part of the name or address of a frequent correspondent, and the system completes the email address.  So convenient! Except when the completion is wrong and you do not check it.

I frequently receive email that was misaddressed because of unchecked completion. (The latest case was last week.) Here is an example of completion that was expected but did not happen. A few years ago, in an institution of which I was then a member, a high-level executive wanted to send to her secretary the results of a job candidate’s evaluation. Highly confidential stuff. The secretary was called Allison and had an address of the form <allison@our_department.com>. The executive was used to typing just all and relying on address completion. Somehow, that particular time the completion did not occur; perhaps she was not using her familiar email setup. As a result the message went to <all@our_department.com>, that is to say, everyone in the organization. The recipients were, of course, delighted to get the inside story on the candidate.

Rule 3:

  • Always check the recipient addresses visually and carefully.
  • Never hire a secretary called Allen, Allison or Allistair.
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Speech synthesis technology

Rereading an article from last year’s August New Yorker, a discussion of e-paper devices and especially the Kindle by Nicholson Baker [1]:

Reading some of “Max,” a James Patterson novel, I experimented with the text-to-speech feature. The robo-reader had a polite, halting, Middle European intonation, like Tom Hanks in “The Terminal,” and it was sometimes confused by periods. Once it thought “miss.” was the abbreviation of a state name: “He loved the chase, the hunt, the split-second intersection of luck and skill that allowed him to exercise his perfection, his inability to Mississippi.” I turned the machine off.

Reference

Nicholson Baker: A New Page, in The New Yorker, August 3, 2009, pages 24-30, also available at http://www.newyorker.com/reporting/2009/08/03/090803fa_fact_baker?currentPage=all.

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Another DOSE of distributed software development

The software world is not flat; it is multipolar. Gone are the days of one-site, one-team developments. The increasingly dominant model today is a distributed team; the place where the job gets done is the place where the appropriate people reside, even if it means that different parts of the job get done in different places.

This new setup, possibly the most important change to have affected the practice of software engineering in this early part of the millennium,  has received little attention in the literature; and even less in teaching techniques. I got interested in the topic several years ago, initially by looking at the phenomenon of outsourcing from a software engineering perspective [1]. At ETH, since 2004, Peter Kolb and I, aided by Martin Nordio and Roman Mitin, have taught a course on the topic [2], initially called “software engineering for outsourcing”. As far as I know it was the first course of its kind anywhere; not the first course about outsourcing, but the first to explore the software engineering implications, rather than business or political issues. We also teach an industry course on the same issues [3], attended since 2005 by several hundred participants, and started, with Mathai Joseph from Tata Consulting Services, the SEAFOOD conference [4], Software Engineering Advances For Outsourced and Offshore Development, whose fourth edition starts tomorrow in Saint Petersburg.

After a few sessions of the ETH course we realized that the most important property of the mode of software development explored in the course is not that it involves outsourcing but that it is distributed. In parallel I became directly involved with highly distributed development in the practice of Eiffel Software’s development. In 2007 we renamed the ETH course “Distributed and Outsourced Software Engineering” (DOSE) to acknowledge the broadened scope. The topic is still new; each year we learn a little more about what to teach and how to teach it.

The 2007 session saw another important addition. We felt it was no longer sufficient to talk about distributed development, but that students should practice it. Collaboration between groups in Zurich and other groups in Zurich was not good enough. So we contacted colleagues around the world interested in similar issues, and received an enthusiastic response. The DOSE project is itself distributed: teams from students in different universities collaborate in a single development. Typically, we have two or three geographically distributed locations in each project group. The participating universities have been Politecnico di Milano (where our colleagues Carlo Ghezzi and Elisabetta di Nitto have played a major role in the current version of the project), University of Nijny-Novgorod in Russia, University of Debrecen in Hungary, Hanoi University of Technology in Vietnam, Odessa National Polytechnic in the Ukraine and (across town for us) University of Zurich. For the first time in 2010 a university from the Western hemisphere will join: University of Rio Cuarto in Argentina.

We have extensively studied how the projects actually fare (see publications [4-8]). For students, the job is hard. Often, after a couple of weeks, many want to give up: they have trouble reaching their partner teams, understanding their accents on Skype calls, agreeing on modes of collaboration, finalizing APIs, devising a proper test plan. Yet they hang on and, in most cases, succeed. At the end of the course they tell us how much they have learned about software engineering. For example I know few better way of teaching the importance of carefully documented program interfaces — including contracts — than to ask the students to integrate their modules with code from another team halfway around the globe. This is exactly what happens in industrial software development, when you can no longer rely on informal contacts at the coffee machine or in the parking lot to smooth out misunderstandings: software engineering principles and techniques come in full swing. With DOSE, students learn and practice these fundamental techniques in the controlled environment of a university project.

An example project topic, used last year, was based on an idea by Martin Nordio. He pointed out that in most countries there are some card games played in that country only. The project was to program such a game, where the team in charge of the game logic (what would be the “business model” in an industrial project) had to explain enough of their country’s game, and abstractly enough, to enable the other team to produce the user interface, based on a common game engine started by Martin. It was tough, but some of the results were spectacular, and these are students who will not need more preaching on the importance of specifications.

We are currently preparing the next session of DOSE, in collaboration with our partner universities. The more the merrier: we’d love to have other universities participate, including from the US. Adding extra spice to the project, the topic will be chosen among those from the ICSE SCORE competition [9], so that winning students have the opportunity to attend ICSE in Hawaii. If you are teaching a suitable course, or can organize a student group that will fit, please read the project description [10] and contact me or one of the other organizers listed on the page. There is a DOSE of madness in the idea, but no one, teacher or student,  ever leaves the course bored.

References

[1] Bertrand Meyer: Offshore Development: The Unspoken Revolution in Software Engineering, in Computer (IEEE), January 2006, pages 124, 122-123. Available here.

[2] ETH course page: see here for last year’s session (description of Fall 2010 session will be added soon).

[3] Industry course page: see here for latest (June 2010( session (description of November 2010 session will be added soon).

[4] SEAFOOD 2010 home page.

[5] Bertrand Meyer and Marco Piccioni: The Allure and Risks of a Deployable Software Engineering Project: Experiences with Both Local and Distributed Development, in Proceedings of IEEE Conference on Software Engineering & Training (CSEE&T), Charleston (South Carolina), 14-17 April 2008, ed. H. Saiedian, pages 3-16. Preprint version  available online.

[6] Bertrand Meyer:  Design and Code Reviews in the Age of the Internet, in Communications of the ACM, vol. 51, no. 9, September 2008, pages 66-71. (Original version in Proceedings of SEAFOOD 2008 (Software Engineering Advances For Offshore and Outsourced Development,  Lecture Notes in Business Information Processing 16, Springer Verlag, 2009.) Available online.

[7] Martin Nordio, Roman Mitin, Bertrand Meyer, Carlo Ghezzi, Elisabetta Di Nitto and Giordano Tamburelli: The Role of Contracts in Distributed Development, in Proceedings of SEAFOOD 2009 (Software Engineering Advances For Offshore and Outsourced Development), Zurich, June-July 2009, Lecture Notes in Business Information Processing 35, Springer Verlag, 2009. Available online.

[8] Martin Nordio, Roman Mitin and Bertrand Meyer: Advanced Hands-on Training for Distributed and Outsourced Software Engineering, in ICSE 2010: Proceedings of 32th International Conference on Software Engineering, Cape Town, May 2010, IEEE Computer Society Press, 2010. Available online.

[9] ICSE SCORE 2011 competition home page.

[10] DOSE project course page.

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Analyzing a software failure

More than once I have emphasized here [1] [2] the urgency of rules requiring systematic a posteriori analysis of software mishaps that have led to disasters. I have a feeling that many more posts will be necessary before the idea registers.

Some researchers are showing the way. In a June 2009 article [4], Tetsuo Tamai from the University of Tokyo published a fascinating dissection of the 2005 Mizuo Securities incident at the Tokyo Stock Exchange, where market havoc resulted from a software fault that prevented proper execution of the cancel command after an employee who wanted to sell one share at 610,000 yen mistakenly switched the two numbers.

I found out only recently about the article while browsing Dines Bjørner’s page and hitting on an unpublished paper [3] where Bjørner proposes a mathematical model for the trading rules. Tamai’s article deserves to be widely read.

References

[1] The one sure way to advance software engineering: this blog, see here.
[2] Dwelling on the point: this blog, see here.
[3] Dines Bjørner: The TSE Trading Rules, version 2, unpublished report, 22 February 2010, available online.
[4] Tetsuo Tamai: Social Impact of Information System Failures, in IEEE Computer, vol. 42, no. 6, June 2009, pages 58-65, available online (with registration); the article’s text is also included in [3].

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PGT-PPP (Pretty Good Translation, Pretty Poor Privacy)

What’s in a URL? To someone who gains access to your computer, your browsing history will provide interesting information. More interesting, in some cases, than you might think.

Try Google Translate, for example. Say you want to translate “To be or not to be, that is the question” into the language of your choice. Go to http://translate.google.com, type your text, select the source language (actually you can skip this step, the tool will detect the language automatically) and the target language (that you will have to do, Google does not read into your mind yet). You get the translation; rather, a Pretty Good Translation, almost never quite right in my experience, but sufficient to give you a Pretty Good Idea. This is the result of modern work in computational linguistics, based on statistics and large-scale data mining rather than a traditional syntax-directed attempt at perfection.

Now look into the URL:

    http://translate.google.com/#auto|sv|To%20be%20or%20not%20to%20be%2C%20that%20is%20the%20question

Your text is encoded in it! This is true even for very long texts. Building up such complex URLs is one of the time-honored techniques for simulating state in the stateless HTTP protocol. But now anyone who sees your browsing history will know the precise texts that interested you enough to make you want to translate them. A Pretty Good Window on your personal interests! And not necessarily something that you want automatically archived.

Google Translate and other translation sites are great tools to facilitate our life, especially when dealing with languages we know superficially or not at all. But maybe there is a way to provide the service without opening such a large window on the detailed questions that occupy our minds?

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Programming on the cloud?

I am blogging live from the “Cloud Futures” conference organized by Microsoft in Redmond [1]. We had two excellent keynotes today, by Ed Lazowska [1] and David Patterson.

Lazowska emphasized the emergence of a new kind of science — eScience — based on analysis of enormous amounts of data. His key point was that this approach is a radical departure from “computational science” as we know it, based mostly on large simulations. With the eScience paradigm, the challenge is to handle the zillions of bytes of data that are available, often through continuous streams, in such fields as astronomy, oceanography or biology. It is unthinkable in his view to process such data through super-computing architectures specific to an institution; the Cloud is the only solution. One of the reasons (developed more explicitly in Patterson’s talk) is that cloud computing supports scaling down as well as scaling up. If your site experiences sudden bursts of popularity — say you get slashdotted — followed by downturns, you just cannot size the hardware right.

Lazowska also noted that it is impossible to convince your average  university president that Cloud is the way to go, as he will get his advice from the science-by-simulation  types. I don’t know who the president is at U. of Washington, but I wonder if the comment would apply to Stanford?

The overall argument for cloud computing is compelling. Of course the history of IT is a succession of swings of the pendulum between centralization and delocalization: mainframes, minis, PCs, client-server, “thin clients”, “The Network Is The Computer” (Sun’s slogan in the late eighties), smart clients, Web services and so on. But this latest swing seems destined to define much of the direction of computing for a while.

Interestingly, no speaker so far has addressed issues of how to program reliably for the cloud, even though cloud computing seems only to add orders of magnitude to the classical opportunities for messing up. Eiffel and contracts have a major role to play here.

More generally the opportunity to improve quality should not be lost. There is a widespread feeling (I don’t know of any systematic studies) that a non-negligible share of results generated by computational science are just bogus, the product of old Fortran programs built by generations of graduate students with little understanding of software principles. At the very least, moving to cloud computing should encourage the use of 21-th century tools, languages and methods. Availability on the cloud should also enhance a critical property of good scientific research: reproducibility.

Software engineering is remarkably absent from the list of scientific application areas that speaker after speaker listed for cloud computing. Maybe software engineering researchers are timid, and do not think of themselves as deserving large computing resources; consider, however, all the potential applications, for example in program verification and empirical software engineering. The cloud is a big part of our own research in verification; in particular the automated testing paradigm pioneered by AutoTest [3] fits ideally with the cloud and we are actively working in this direction.

Lazowska mentioned that development environments are the ultimate application of cloud computing. Martin Nordio at ETH has developed, with the help of Le Minh Duc, a Master’s student at Hanoi University of Technology, a cloud-based version of EiffelStudio: CloudStudio, which I will present in my talk at the conference tomorrow. I’ll write more about it in later posts; just one note for the moment: no one should ever be forced again to update or commit.

References

[1] Program of the Cloud Futures conference.

[2] Keynote by Ed Lazowska. You can see his slides here.

[3] Bertrand Meyer, Arno Fiva, Ilinca Ciupa, Andreas Leitner, Yi Wei, Emmanuel Stapf: Programs That Test Themselves. IEEE Computer, vol. 42, no. 9, pages 46-55, September 2009; online version here.

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Dwelling on the point

Once again, and we are not learning!

La Repubblica of last Thursday [1] and other Italian newspapers have reported on a “computer” error that temporarily brought thousands of accounts at the national postal service bank into the red. It is a software error, due to a misplacement of the decimal points in some transactions.

As usual the technical details are hazy; La Repubblica writes that:

Because of a software change that did not succeed, the computer system did not always read the decimal point during transactions”.

As a result, it could for example happen that a 15.00-euro withdrawal was understood as 1500 euros.
I have no idea what “reading the decimal point ” means. (There is no mention of OCR, and the affected transactions seem purely electronic.) Only some of the 12 million checking or “Postamat” accounts were affected; the article cites a number of customers who could not withdraw money from ATMs because the system wrongly treated their accounts as over-drawn. It says that this was the only damage and that the postal service will send a letter of apology. The account leaves many questions unanswered, for example whether the error could actually have favored some customers, by allowing them to withdraw money they did not have, and if so what will happen.

The most important unanswered question is the usual one: what was the software error? As usual, we will probably never know. The news items will soon be forgotten, the postal service will somehow fix its code, life will go on. Nothing will be learned; the next time around similar causes will produce similar effects.

I criticized this lackadaisical attitude in an earlier column [2] and have to hammer its conclusion again: any organization using public money should be required, when it encounters a significant software malfunction, to let experts investigate the incident in depth and report the results publicly. As long as we keep forgetting our errors we will keep repeating them. Where would airline safety be in the absence of thorough post-accident reports? That a software error did not kill anyone is not a reason to ignore it. Whether it is the Italian post messing up, a US agency’s space vehicle crashing on the moon or any other software fault causing systems to fail, it is not enough to fix the symptoms: we must have a professional report and draw the lessons for the future.

Reference

[1] Luisa Grion: Poste in tilt per una virgola — conti gonfiati, stop ai prelievi. In La Repubblica, 26 November 2009, page 18 of the print version. (At the time of writing it does not appear at repubblica.it,  but see  the TV segment also titled “Poste in tilt per una virgola” on Primocanale Web TV here, and other press articles e.g. in Il Tempo here.)

[2] On this blog: The one sure way to advance software engineering (post of 21 August 2009).

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Methods need theory

For someone in search of a software development method, the problem is not to find answers; it’s to find out how good the proposed answers are. We have lots of methods — every year brings its new harvest — but the poor practitioner is left wondering why last year’s recipe is not good enough after all, and why he or she has to embrace this year’s buzz instead. Anyone looking for serious conceptual arguments has to break through the hype and find the precious few jewels of applicable wisdom.

This is the start of an article that Ivar Jacobson and I just wrote for Dr. Dobb’s Journal;  it is available in the online edition [1] and will appear (as I understand) in the next paper edition. The article is a plea for a rational, science-based approach to software development methodology, and a call for others to join us in establishing a sound basis.

Reference

[1] Ivar Jacobson and Bertrand Meyer, Methods Need Theory, Dr. Dobb’s Journal, August 2009, available online.

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Computer technology: making mozzies out of betties

Are you a Beethoven or a Mozart? If you’ll pardon the familarity, are you more of a betty or more of a mozzy? I am a betty. I am not referring to my musical abilities but to my writing style; actually, not the style of my writings (I haven’t completed any choral fantasies yet) but the style of my writing process. Mozart is famous for impeccable manuscripts; he could be writing in a stagecoach bumping its way through the Black Forest, on the kitchen table in the miserable lodgings of his second, ill-fated Paris trip, or in the antechamber of Archbishop Colloredo — no matter: the score comes out immaculate, not reflecting any of the doubts, hesitations and remorse that torment mere mortals. 

 Mozart

Beethoven’s music, note-perfect in its final form, came out of a very different process. Manuscripts show notes overwritten, lines struck out in rage, pages torn apart. He wrote and rewrote and gave up and tried again and despaired and came back until he got it the way it had to be.

Beethoven

How I sympathize! I seldom get things right the first time, and when I had to use a pen and paper I  almost never could produce a clean result; there always was one last detail to change. As soon as I could, I got my hands on typewriters, which removed the effects of ugly handwriting, but did not solve the problem of second thoughts followed by third thoughts and many more. Only with computers did it become possible to work sensibly. Even with a primitive text editor, the ability to try out ideas then correct and correct and correct is a profound change of the creation process. Once you have become used to the electronic medium, using a pen and paper seems as awkard and insufferable as, for someone accustomed to driving a car, being forced to travel in an oxen cart.

This liberating effect, the ability to work on your creations as a sculptor kneading an infinitely malleable material, is one of the greatest contributions of computer technology. Here we are talking about text, but the effect is just as profound on other media, as any architect or graphic artist will testify.

The electronic medium does not just give us more convenience; it changes the nature of writing (or composing, or designing). With paper, for example, there is a great practical difference between introducing new material at the end of the existing text,  which is easy, and inserting it at some unforeseen position, which is cumbersome and sometimes impossible. With computerized tools, it doesn’t matter. The change of medium changes the writing process and ultimately the writing: with paper the author ends up censoring himself to avoid practically painful revisions; with software tools, you work in whatever order suits you.

Technical texts, with their numbered sections and subsections, are another illustration of the change: with a text processor you do not need to come up with the full plan first, in an effort to avoid tedious renumbering later. You will use such a top-down scheme if it fits your natural way of working, but you can use any other  one you like, and renumber the existing sections at the press of a key. And just think of the pain it must have been to produce an index in the old days: add a page (or, worse, a paragraph, since it moves the following ones in different ways) and you would  have to recheck every single entry.

Recent Web tools have taken this evolution one step further, by letting several people revise a text collaboratively and concurrently (and, thanks to the marvels of  longest-common-subsequence algorithms and the resulting diff tools, retreat to an earlier version if in our enthusiasm to change our design we messed it up) . Wikis and Google Docs are the most impressive examples of these new techniques for collective revision.

Whether used by a single writer or in a collaborative development, computer tools have changed the very process of creation by freeing us from the tyranny of physical media and driving to zero the logistic cost of  one or a million changes of mind. For the betties among us, not blessed with an inborn ability to start at A, smoothly continue step by step, and end at Z, this is a life-changer. We can start where we like, continue where we like, and cover up our mistakes when we discover them. It does not matter how messy the process is, how many virtual pages we tore away, how much scribbling it took to bring a paragraph to a state that we like: to the rest of the world, we can present a result as pristine as the manuscript of a Mozart concerto.

These advances are not appreciated enough; more importantly, we do not take take enough advantage of them. It is striking, for example, to see that blogs and other Web pages too often remain riddled with typos and easily repairable mistakes. This is undoubtedly because the power of computer technology tempts us to produce ever more documents and in the euphoria to neglect the old ones. But just as importantly that technology empowers  us to go back and improve. The old schoolmaster’s advice — revise and revise again [1] — can no longer  be dismissed as an invitation to fruitless perfectionism; it is right, it is fun to apply, and at long last it is feasible.

Reference

 

[1] “Vingt fois sur le métier remettez votre ouvrage” (Twenty times back to the loom shall you bring your design), Nicolas Boileau

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The understated breakthrough

You must have seen articles in that genre: the author collects a few  imprudent technology predictions from the past and pokes fun at their authors, the more prestigious the better. Favorites — copy-pasted, with or without fact-checking, from one  such piece to another — are Lord Kelvin’s 1895 pronouncement that “Heavier-than-air flying machines are impossible” and Bill Gates’s comment (apocryphal, but who cares?) that no one needs more than 640KB of memory. Run a Bing search for something like “wrong technology predictions” and you will find dozens of such collections.

A few years ago they often derided turn-of-the-previous-century visions of communication through a videophone, as in this depiction by Villemard:

 
Villemard_videophone

 
A bit one-sided in its view of gender roles, but delightful and amusing. “Look what in 1910 they thought the future would bring!” In 2009 it is no longer laughable: the future is here, and — other than display techniques and women’s fashions — it is exactly this.

I am amazed by the lack of hype that has been associated with Skype. With close to 500 million accounts, and 17 million of them connected at a typical time, it is not exactly a secret; but it is remarkable how little buzz it gets in the media and in our collective consciousness.  The company itself seems to be more interested in getting the job done than in glamour. If we look at the substance, however, few technologies come to mind from the past few decades that have influenced people’s lives so directly and beneficially. From the launch of Skype in 2003 it became possible to have free calls worldwide;  then in 2005 free video was included. Suddenly this videotelephony, fodder for visionaries and cartoonists, became available to anyone with an Internet connection — for free! Almost as unbelievable as the technical feat is that this all happened without headlines, without grandiose pronouncements, and without any forewarning by the technology pundits. Almost overnight we take a giant collective step, and we act as if nothing happened.

With and without video, Skype has had a profound effect on the daily communications of countless people. It also has many professional applications; in an article of last year [2] I described how we use it, together with other technologies such as Google Docs, to turn the old “Code inspection” of software engineering into something far more useful to the project and attractive to the participants.

As always with a breakthrough technology, you find the naysayers; in the universities of a large Western country, system administrators have — can anyone believe this? — banned the use of  Skype, invoking some mysterious and unsubstantiated security risks. And certainly Skype is not perfect; we still get the occasional dropped call. What is more relevant than the occasional annoyance is how well the technology is designed; I have used the audio part, with quite reasonable performance, on a dial-up line from a remote location. And successive versions keep bringing in new wonders.

If there was an award for the highest usefulness to brouhaha ratio, Skype would be the favorite. Cheers for the most practical, least hyped technology of our time.

References

 

[1] Futuristic postcards by Villemard, from an exhibition.

[2] Design and Code Reviews in the Age of the Internet (preprint of an article in Communications of the ACM, Sept. 2008).

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