Archive for November 2011

Ado About The Resource That Was (Not)

 

After a few weeks of use, Microsoft Outlook tends in my experience to go into a kind of thrashing mode where the user interface no longer quite functions as it should, although to the tool’s credit it does not lose information. Recently I have been getting pop-up warnings such as

 

A required resource was

 

A required resource was what ? The message reminded me of an episode in a long-ago game of Scrabble, in which I proposed ADOABOUT as a word. “Ado about what? ”, the other players asked, and were not placated by my answer.

The message must have been trying to say  that a required resource was missing, or not found, but at the time of getting the final detail Outlook must have run out of UI resources and hence could not summon the needed text string. Not surprising, since running out of resources is precisely what caused the message to appear, in a valiant attempt to tell the user what is going on. (Valiant but not that useful: if you are not a programmer on the Outlook development team but just a customer trying to read email, it is not absolutely obvious how the message, even with the missing part, helps you.) The irony in the example is that the title bar suggests the problem arose in connection with trying to display the “Social Connector” area, a recent Outlook feature which I have never used. (Social connector? Wasn’t the deal about getting into computer science in the first place that for the rest of your life you’d be spared the nuisance of social connections? One can no longer trust anything nowadays.)

We can sympathize with whoever wrote the code. The Case Of The Resource That Was (Not) is an example of a general programming problem which we may call Space Between Your Back And Wall  or SBYBAW:  when you have your back against the wall, there is not much maneuvering space left.

A fairly difficult case of the SBYBAW problem arises in garbage collection, for example for object-oriented languages. A typical mark-and-sweep garbage collector must traverse the entire object structure to remove all the objects that have not been marked as reachable from the stack. The natural way to write a graph traversal algorithm is recursive: visit the roots; then recursively traverse their successors, flagging visited objects in some way to avoid cycling. Yes, but the implementation of a recursive routine relies on a stack of unpredictable size (the longest path length). If we got into  garbage collection, most likely it’s that we ran out of memory, precisely the kind of situation in which we cannot afford room for unpredictable stack growth.

In one of the early Eiffel garbage collectors, someone not aware of better techniques had actually written the traversal recursively; had the mistake not been caught early enough, it would no doubt have inflicted unbearable pain on humankind. Fortunately there is a solution: the Deutsch-Schorr-Waite algorithm [1], which avoids recursion on the program side by perverting the data structure to  replace some of the object links by recursion-control links; when the traversal’s execution proceeds along an edge, it reverses that edge to permit eventual return to the source. Strictly speaking, Deutsch-Schorr-Waite still requires a stack of booleans — to distinguish original edges from perverted ones — but we can avoid a separate stack (even just  a stack of booleans, which can be compactly represented in a few integers) by storing these booleans in the mark field of the objects themselves. The resulting traversal algorithm is a beauty — although it is fairly tricky, presents a challenge for verification tools, and raises new difficulties in a multi-threaded environment.

Deutsch-Schorr-Waite is a good example of “Small Memory Software” as studied in a useful book of the same title [2]. The need for Small Memory Software does not just arise for embedded programs running on small devices, but also in mainstream programming whenever we face the SBYBAW issue.

The SBYBAW lesson for the programmer is tough but simple. The resources we have at our disposal on a computing system may be huge, but they are always finite, and our programs’ appetite for resources will eventually exhaust them. At that stage, we have to deal with the SBYBAW rule, which sounds like a tautology but is an encouragement to look for clever algorithms:  techniques for freeing resources when no resources remain must not request new resources.

References

[1] Deutsch-Schorr-Waite is described in Knuth and also in [2]. Someone should start a Wikipedia entry.

[2] James Noble and Charles Weir: Small Memory Software: Patterns for Systems with Limited Memory, Addison-Wesley, 2001.

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The Modes and Uses of Scientific Publication

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Recycled(This article was initially published in the CACM blog.)
Publication is about helping the advancement of humankind. Of course.

Let us take this basis for granted and look at the other, possibly less glamorous aspects.

Publication has four modes: Publicity; Exam; Business; and Ritual.

1. Publication as Publicity

The first goal of publication is to tell the world that you have discovered something: “See how smart I am!” (and how much smarter than all the others out there!). In a world devoid of material constraints for science, or where the material constraints are handled separately, as in 19th-century German universities where professors were expected to fund their own labs, this would be the only mode and use of publication. Science today is a more complex edifice.

A good sign that Publication as Publicity is only one of the modes is that with today’s technology we could easily skip all the others. If all we cared about were to make our ideas and results known, we would simply put out our papers on ArXiv or just our own Web page. But almost no one stops there; researchers submit to conferences and journals, demonstrating how crucial the other three modes are to the modern culture of science.

2. Publication as Exam

Academic careers depend on a publication record. Actually this is not supposed to be the case; search and tenure committees are officially interested in “impact,” but any candidate is scared of showing a short publication list where competitors have tens or (commonly) hundreds of items.

We do not just publish; we want to be chosen for publication. Authors are proud of the low acceptance rates of conferences at which their papers have been accepted; in the past few years it has in fact become common practice, in publication lists attached to CVs, to list this percentage next to each accepted article. Acceptance rates are carefully tracked; see for example [2] for software engineering.

As Jeff Naughton has pointed out [1], this mode of working amounts to giving researchers the status of students forced to take exams again and again. Maybe that part is inevitable; the need to justify ourselves anew every morning may be an integral part of being a scientist, especially one funded by other people’s money. Two other consequences of this phenomenon are, I believe, more damaging.

The first risk directly affects the primary purpose of publication (remember the advancement of humankind?): a time-limited review process with low acceptance rates implies that some good papers get rejected and some flawed ones accepted. Everyone in software engineering knows (and recent PC chairs have admitted) that getting a paper accepted at the International Conference on Software Engineering is in part a lottery; with an acceptance rate hovering around 13%, this is inevitable. The mistakes occur both ways: papers accepted or even getting awards, then shown a few months later to be inaccurate; and innovative papers getting rejected because some sentence rubbed the referees the wrong way, or some paper was not cited. With a 4-month review cycle, and the next deadline coming several months later, the publication of a truly important result can be delayed significantly.

The second visible damage is publication inflation. Today’s research environment channels productive research teams towards an LPU (Least Publishable Unit) publication practice, causing an explosion of small contributions and the continuous decrease of the ratio of readers to writers. When submitting a paper I have always had, as my personal goal, to be read; but looking at the overall situation of computer science publication today suggests that this is not the dominant view: the overwhelming goal of publication is publication.

3. Publication as Business

Publishing requires an infrastructure, and money plays a role. Conferences in particular are a business. They have a budget to balance, not always an easy task, although a truly successful conference can be a big money-maker for its sponsor, commercial or non-profit. The financial side of conference publication has its consequences on authors: if you do not pay your fees, not only will you be unable to participate, but your paper will not be published.

One can deplore these practices, in particular their effect on authors from less well-endowed institutions, but they result from today’s computer science publication culture with its focus on the conference, what Lance Fortnow has called “A Journal in a Hotel”.

Sometimes the consequences border on the absurd. The ASE conference (Automated Software Engineering) accepts some contributions as “short papers”. Fair enough. At ASE 2009, “short paper” did not mean a shorter conference presentation but the permission to put up a poster and stand next to it for a while and answer passersby’s questions. For that privilege — and the real one: a publication in the conference volume — one had to register for the conference. ASE 2009 was in New Zealand, the other end of the world for a majority of authors. I ceded to the injunction: who was I to tell the PhD student whose work was the core of the submission, and who was so happy to have a paper accepted at a well-ranked conference, that he was not going to be published after all? But such practices are dubious. It would be more transparent to set up an explicit pay-for-play system, with page charges: at least the money would go to a scientific society or a university. Instead we ended up funding (in addition to the conference, which from what I heard was an excellent experience) airlines and hotels.

What makes such an example remarkable is that a reasonable justification exists for every one of its components: a highly selective refereeing process to maintain the value of the publication venue; limiting the number of papers selected for full presentation, to avoid a conference with multiple parallel tracks (and the all too frequent phenomenon of conference sessions whose audience consists of the three presenters plus the session chair); making sure that authors of published papers actually attend the event, so that it is a real conference with personal encounters, not just an opportunity to increment one’s publication count. The concrete result, however, is that authors of short papers have the impression of being ransomed without getting the opportunity to present their work in a serious way. Literally seconds as I was going to hit the “publish” button for the present article, an author of an accepted short paper for ASE 2012 (where the process appears similar) sent an email to complain, triggering a new discussion. We clearly need to find better solutions to resolve the conflicting criteria.

4. Publication as Ritual

Many of the seminal papers in science, including some of the most influential in computer science, defy classification and used a distinctive, one-of-a-kind style. Would they stand a chance in one of today’s highly ranked conferences, such as ICSE in software or VLDB in databases? It’s hard to guess. Each community has developed its own standard look-and-feel, so that after a while all papers start looking the same. They are like a classical mass with its Te Deum, Agnus Dei and Kyrie Eleison. (The “Te Deum” part is, in a conference submission, spread throughout the paper, in the form of adoring citations of the program committee members’ own divinely inspired articles, good for their H-indexes if they bless your own offering.)

All empirical software engineering papers, for example, have the obligatory “Threats to Validity” section, which is has developed into a true art form. The trick is the same as in the standard interview question “What can you say about your own deficiencies?”, to which every applicant know the key: describe a personality trait so that you superficially appear self-critical but in reality continue boasting, as in “sometimes I take my work too much to heart” [3]. The “Threats to Validity” section follows the same pattern: you try to think of all possible referee objections, the better to refute them.

Another part of the ritual is the “related work” section, treacherous because you have to make sure not to omit anything that a PC member finds important; also, you must walk a fine line between criticizing existing research too much, which could offend someone, or not enough, which enables the referee to say that you are not bringing anything significantly new. I often wonder who, besides the referees, reads those sections. But here too it is easier to lament than to fault the basic idea or propose better solutions. We do want to avoid wasting our time on papers whose authors are not aware of previous work. The related work section allows referees to perform this check. Its importance in the selection process has, however, grown out of proportion. It is one thing to make sure that a paper is state-of-the-art, but another to reject it (as often happens) because it fails to cite a particular contribution whose results would not directly affect its own. Here we move from the world of the rational to the world of the ritual. An extreme and funny recent example — funny to me, not necessarily to the coauthors — is a rejection from  APSEC 2011, the Australia-Pacific Software Engineering Conference, based on one review (the others were positive) that stated: “How novel is this? Are [there] not any cloud-based IDEs out there that have [a] similar awareness model integrated into their CM? This is something the related work [section] fails to describe precisely. [4] The ritual here becomes bizarre: as far as we know, no existing system discusses a similar model; the reviewer too does not know of any; but he blasts the paper all the same for not citing work that he thinks must have been done by someone, somehow, somewhere. APSEC is a fine conference — it has to be, from the totally unbiased criterion that it accepted another one of our submissions this year! — and this particular paper may or may not have been ready for publication; judge it for yourself [5]. Such examples suggest, however, that the ritual of computer science publication has its limits.

Publicity, Exam, Business, Ritual: to which one of the four modes of publication are you most attuned? Oh, sorry, I forgot: in your case, it is solely for the advancement of humankind.

References and notes

[1] Jeffrey F. Naughton, DBMS Research: First 50 Years, Next 50 Years, slides of keynote at 26th IEEE International Conference on Data Engineering, 2010, available at lazowska.cs.washington.edu/naughtonicde.pdf .

[2] Tao Xie, Software Engineering Conferences, at people.engr.ncsu.edu/txie/seconferences.htm .

[3] I once saw on French TV a hilarious interview of an entrepreneur who had started a software company in Vietnam, where job candidates just did not know “the code”, and moved on, in response to such a question, to tell the interviewer about being rude to their mother and all the other horrible things they had done in their lives.

[4] The words in brackets were not in the review but I added them for clarity.

[5] Martin Nordio, H.-Christian Estler, Carlo A. Furia and Bertrand Meyer: Collaborative Software Development on the Web, available at arxiv.org/abs/1105.0768 .

(This article was first published on the CACM blog in September 2011.)

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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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John McCarthy

John McCarthyJohn McCarthy, who died last week at the age of 84, was one of the true giants of computer science. Most remarkable about his contributions are their diversity, their depth, and how they span both theory and practice.

To talk about him it is necessary first to dispel an unjustly negative connotation. McCarthy was one of the founders of the discipline of artificial intelligence, its most forceful advocate and the inventor of its very name. In the “AI Winter” episode of the late 1970s and 1980s, that name suffered some disrepute as a result of a scathing report by James Lighthill blaming AI researchers for over-promising. In fact the promoters of AI may not have delivered exactly what they announced (who can accurately predict science?); but what they delivered is astounding. Many breakthroughs in computer science, both in theory (advances in lambda calculus and the theory of computation) and in the practice of programming (garbage collection, functional programming languages), can directly be traced to work in AI. Part of the problem is a phenomenon that I heard John McCarthy himself describe:  “As soon as it works, no one calls it AI any more.” Automatic garbage collection was once advanced artificial intelligence; now it is just an algorithm that makes sure your smartphone does not freeze up. In a different field, we have become used to computers routinely beating chess champions, a feat that critics of AI once deemed unthinkable.

The worst over-promises came not from researchers in the field such as McCarthy, who understood the difficulties, but from people like Herbert Simon, more of a philosopher, who in 1965 wrote that “machines will be capable, within twenty years, of doing any work a man can do.” McCarthy’s own best-known over-promise was to take up David Levy on his 1968 bet that no computer would be able to beat him within ten years. But McCarthy was only mistaken in under-estimating the time span: Deep Blue eventually proved him right.

The word that comes most naturally to mind when thinking about McCarthy is “brilliant.” He belonged to that category of scientists who produce the fundamental insights before anyone else, even if they do not always have the patience to finalize the details. The breathtaking paper that introduced Lisp [1] is labeled “Part 1”; there was never a “Part 2.” (Of course we have a celebrated example in computer science, this one from a famously meticulous author, of a seven-volume treaty which never materialized in full.) It was imprudent to announce a second part, but the first was enough to create a whole new school of programming. The Lisp 1.5 manual [2], published in 1962, was another masterpiece; as early as page 13 it introduces — an unbelievable feat, especially considering that the program takes hardly more than half a page — an interpreter for the language being defined, written in that very language! The more recent reader can only experience here the kind of visceral, poignant and inextinguishable jealously that overwhelms us the first time we realize that we will never be able to attend the première of Don Giovanni at the Estates Theater in Prague on 29 October, 1787 (exactly 224 years ago yesterday — did you remember to celebrate?). What may have been the reaction of someone in “Data Processing,” such as it was in 1962, suddenly coming across such a language manual?

These years, 1959-1963, will remain as McCarthy’s Anni Mirabiles. 1961 and 1962 saw the publication of two visionary papers [3, 4] which started the road to modern program verification (and where with the benefit of hindsight it seems that he came remarkably close to denotational semantics). In [4] he wrote

Instead of debugging a program, one should prove that it meets its specifications, and this proof should be checked by a computer program. For this to be possible, formal systems are required in which it is easy to write proofs. There is a good prospect of doing this, because we can require the computer to do much more work in checking each step than a human is willing to do. Therefore, the steps can be bigger than with present formal systems.

Words both precise and prophetic. The conclusion of [3] reads:

It is reasonable to hope that the relationship between computation and mathematical logic will be as fruitful in the next century as that between analysis and physics in the last. The development of this relationship demands a concern for both applications and for mathematical elegance.

“A concern for both applications and mathematical elegance” is an apt characterization of McCarthy’s own work. When he was not busy designing Lisp, inventing the notion of meta-circular interpreter and developing the mathematical basis of programming, he was building the Lisp garbage collector and proposing the concept of time-sharing. He also played, again in the same period, a significant role in another milestone development, Algol 60 — yet another sign of his intellectual openness and versatility, since Algol is (in spite of the presence of recursion, which McCarthy championed) an imperative language at the antipodes of Lisp.

McCarthy was in the 1960s and 70s the head of the Artificial Intelligence Laboratory at Stanford. For some reason the Stanford AI Lab has not become as legendary as Xerox PARC, but it was also the home to early versions of  revolutionary technologies that have now become commonplace. Email, which hardly anyone outside of the community had heard about, was already the normal way of communicating, whether with a coworker next door or with a researcher at MIT; the Internet was taken for granted; everyone was using graphical displays and full-screen user interfaces; outside, robots were playing volley-ball (not very successfully, it must be said); the vending machines took no coins, but you entered your login name and received a bill at the end of the month, a setup which never failed to astonish visitors; papers were printed with sophisticated fonts on a laser printer (I remember a whole group reading the successive pages of Marvin Minsky’s  frames paper [5] directly on the lab’s XGP, Xerox Graphics Printer, as  they were coming out, one by one, straight from MIT). Arthur Samuel was perfecting his checkers program. Those who were not programming in Lisp were hooked to SAIL, “Stanford Artificial Intelligence Language,” an amazing design which among other insights convinced me once and for all that one cannot seriously deal with data structures without the benefit of an automatic serialization mechanism. The building itself, improbably set up amid the pastures of the Santa Cruz foothills, was razed in the eighties and the lab moved to the main campus, but the spirit of these early years lives on.

McCarthy ran the laboratory in an open and almost debonair way; he was a legend and somewhat intimidating, but never arrogant and in fact remarkably approachable. I took the Lisp course from him; in my second or third week at Stanford, I raised my hand and with the unflappable assurance of the fully ignorant slowly asked a long question: “In all the recursive function definitions that you have shown so far, termination was obvious because there is some ‘n’ that decreases for every recursive call, and we treat the case ‘n = 0’ or ‘n = 1’ in a special, non-recursive way. But things won’t always be so simple. Is there some kind of grammatical criterion on Lisp programs that we could use to ascertain whether a recursive definition will always lead to a terminating computation?” There was a collective gasp from the older graduate students in the audience, amazed that a greenhorn would have the audacity to interrupt the course with such an incompetent query. But instead of dismissing me, McCarthy proceeded, with a smile, to explain the basics of undecidability. He had the same attitude in the many seminars that he taught, often on topics straddling computer science and philosophy, in a Socratic style where every opinion was welcome and no one was above criticism.

He also had a facetious side. At the end of a talk by McCarthy at SRI, Tony Hoare, who was visiting for a few days, asked a question; McCarthy immediately rejoined that he had expected that question, summoned to the stage a guitar-carrying researcher from the AI Lab, and proceeded with the answer in the form of a prepared song.

The progress of science and technology is a collective effort; it takes many people to turn new insights into everyday reality. The insights themselves come from a few individuals, a handful in every generation. McCarthy was one of these undisputed pioneers.

 

References

[1] John McCarthy: Recursive Functions of Symbolic Expressions and Their Computation by Machine, Part I, in Communications of the ACM, vol. 3, no. 4, 1960, pages 184-195.

[2] John McCarthy, Paul W. Abrahams, Daniel J. Edwards, Timothy P. Hart, Michael I. Levin, LISP 1.5 Programmer’s Manual, MIT, 1962. Available at Amazon  External Linkand also as a PDF External Link.

[3] John McCarthy: A Basis for a Mathematical Theory of Computation, first version in Proc. Western Joint Computer Conference, 1961, revised version in Computer Programming and Formal Systems, eds. P. Braffort and D. Hirschberg, North Holland, 1963. Available in various places on the Web, e.g. here External Link.

[4] John McCarthy: Towards a Mathematical Science of Computation, in IFIP Congress 1962, pages 21-28, available in various places on the Web, e.g. here External Link.

[5] Marvin Minsky:  A Framework for Representing Knowledge, MIT-AI Laboratory Memo 306, June 1974, available here External Link.

 

(This article was first published on my ACM blog.  I am resuming regular Monday publication.)

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