Technology

Fun with Bayes

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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?

Every bilingual dictionary should be a Galois connection

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A Galois connection (for anyone not familiar with the concept, the Wikipedia entry is decent)  between two partially ordered sets consists of two total functions f: A → B and g: B → A such that for  all a: A and b: B

(f (a)  ≤  b)    ⇔    (g  (b)  ≥  a)

The simplest and most common example uses powersets and inclusion: for some sets X and Y, A is ℙ (X), the set of subsets of X, and B is ℙ (Y); the ≤ order relation is simply ⊆, inclusion between subsets. So the condition is that for arbitrary subsets a and b of X and Y:

(f (a)  ⊆  b)    ⇔    (g  (b)  ⊇  a)

Pictorially:

A Galois connection between powersets
A Galois connection between powersets

(Instead of starting with total functions f and g between  ℙ (X) and ℙ (Y) you may also use possibly partial functions f’ : A -|-> B and g’ : B -|-> A, and use for f and g the associated image functions, which are total.)

Now you might think that this post continues with abstract interpretation or some such topic, but what I really want to talk about is dictionaries. Bilingual dictionaries. You need them if you are learning a language, and they would seem to be the ideal application for computers, including shirt-pocket computers (more commonly known as smartphones). Hyperlinking frees us from the tyranny of page turning and makes dictionary browsing an exciting and entirely new experience: you can type partial words and see them completed, make mistakes and see them corrected, discover a new word and see it memorized into the interactive equivalent of flashcards. If in the definition of a word you see another that catches your attention, in either the source or the target language, you can click it and see its own definition. You can travel back and forth, retain your browsing history, and test yourself repeatedly.

Unfortunately, what I have described is only the theory. Current electronic bilingual dictionaries — at least those I tried, but I tried quite a few, involving a variety of languages — fall short of this ideal. In addition, they are typically of rather bad linguistic quality as compared to their print competitors.

An example of a seemingly fundamental requirement that every bilingual dictionary should satisfy (and that dictionaries on the market fail to meet), is that the relationship it defines between two languages must  be a Galois connection, both ways. If you are looking for the translation of a word or group of words a in X, and obtain a set b of equivalents in Y, then it is pretty hard to justify that when you go back the translations for b do not include a!

I have yet, however, to find a Galois dictionary. As an example among hundreds that I encountered in recent months, take the Pons ($30) French-German dictionary. As the names suggest f will be the function yielding the French translation of a set of German words and g  the German translation of a set of French words. Now g {(“approximation“)}  includes “Näherungswert“; but then f ({“Näherungswert“}) only lists “Valeur approchée“!

The Galois requirement is not just a matter of principle; it makes the dictionary useful for native speakers of either language. If, as here,  g  (b)  ⊆  a but (f (a) ⊉    b), and a includes  the most common words in Y for the concepts at hand, the native Y speaker may find the right translation (Näherungswert is indeed pretty good for approximation in the mathematical usage of this word),  but the native speaker of X will be misled. Indeed valeur approchée is not  the best term for the concept of mathematical approximation in French.

More generally, the reader who is trying to master both of the dictionary’s languages will be cheated. Such a reader wants to use the dictionary not just to get quick translations (there’s Google and Bing Translate for that), but to gain deep insights into the languages and their correspondence. How can one learn without the ability to check translations back and forth?

I wrote to Pons to report this problem (and others). To their great credit they took the trouble to answer my message in detail; but here is their tack on the issue:

“As far as the choice of headwords for the source and the target language is concerned, PONS always is doing this choice for each language volume separatedly as we the dictionaries are made for special target groups and in different sizes we have to make a choice of words and this is done with regard to the importance a word has in each language – source and target language – and not by simply changing source and target language. This special elaboration of headword lists for each language can imply that a word which can be found in one volume of the dictionary is not necessarily part of the other volume.”

I am not sure I understand what this means, but I am much too kind to wish upon dictionary authors, if they do not fix their systems, the sad fate of Évariste Galois.

Email and its perils

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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.

Speech synthesis technology

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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.

Programming on the cloud?

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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.

Just another day at the office

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In the past few weeks I wrote a program to compute the aliases of variables and expressions in an object-oriented program (based on a new theory [1]).

For one of the data structures, I needed a specific notion of equality, so I did the standard thing in Eiffel: redefine the is_equal function inherited from the top class ANY, to implement the desired variant.

The first time I ran the result, I got a postcondition violation. The violated postcondition clauses was not even any that I wrote: it was an original postcondition of is_equal (other: like Current)  in ANY, which my redefinition inherited as per the rules of Design by Contract; it reads

symmetric: Result implies other ~ Current

meaning: equality is symmetric, so if Result is true, i.e. the Current object is equal to other, then other must also be equal to Current. (~ is object equality, which applies the local version is is_equal).  What was I doing wrong? The structure is a list, so the code iterates on both the current list and the other list:

from
    start ; other.start ; Result := True
until (not Result) or after loop
        if other.after then Result := False else
              Result := (item ~ other.item)
              forth ; other.forth
        end
end

Simple enough: at each position check whether the item in the current list is equal to the item in the other list, and if so move forth in both the current list and the other one; stop whenever we find two unequal elements, or we exhaust either list as told by after list. (Since is_equal is a function and not produce any side effect, the actual code saves the cursors before the iteration and restores them afterwards. Thanks to Ian Warrington for asking about this point in a comment to this post. The new across loop variant described in  two later postings uses external cursors and manages them automatically, so this business of maintaining the cursor manually goes away.)

The problem is that with this algorithm it is possible to return True if the first list was exhausted but not the second, so that the first list is a subset of the other rather than identical. The correction is immediate: add

Result and other_list.after

after the loop; alternatively, enclose the loop in a conditional so that it is only executed if count = other.count (this solution is  better since it saves much computation in cases of lists of different sizes, which cannot be equal).

The lesson (other than that I need to be more careful) is that the error was caught immediately, thanks to a postcondition violation — and one that I did not even have to write. Just another day at the office; and let us shed a tear for the poor folks who still program without this kind of capability in their language and development environment.

Reference

[1] Bertrand Meyer: The Theory and Calculus of Aliasing, draft paper, available here.

Dwelling on the point

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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).

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.

 

Computer technology: making mozzies out of betties

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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

The understated breakthrough

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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).