Publications

Computing: the Art, the Magic, the Science

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My colleagues and I have just finished recording our new MOOC (online course), an official ETH offering on the EdX platform. The preview is available [1] and the course will run starting in September.

As readers of this blog know, I  have enthusiastically, under the impulsion of Marco Piccioni at ETH, embraced MOOC technology to support and spread our courses. The particular target has been the introduction to programming that I have taught for over a decade at ETH based on the Touch of Class textbook [2]. In February this blog announced [3] the release of our first MOOC, embodying the essentials of our ETH course and making it available not only to ETH students but to the whole world. The course does not just include video lectures: it also supports active student participation through online exercises and programs that can be compiled and tested on the cloud, with no software installation. These advanced features result from our research on support for distributed software development (by Christian Estler and Martin Nordio, with Carlo Furia and others).

This first course was a skunkworks project, which we did entirely on our own without any endorsement from ETH or any of the main MOOC players. We and our students have very much benefited from the consequent flexibility, and the use of homegrown technology relying on the MOODLE framework. We will keep this course for our own students and for any outside participant who prefers a small-scale, “boutique” version. But the EdX brand and EdX’s marketing power will enable us to reach a much broader audience. We want to provide the best introductory computing course on the market and the world needs to know about it. In addition, the full support of media services at ETH  helped us reach a higher standard on the technical side. (For our first course, the home-brewed one, we did not have a studio, so that every time an ambulance drove by — our offices are close to the main Zurich hospital — we had to restart the current take.)

The course’s content is not exactly the same: we have broadened the scope from just programming to computing, although it retains a strong programming component. We introduced additional elements such as an interview with Professor Peter Widmayer of ETH on the basics of computer science theory. For both new material and the topics retained from the first version we have adapted to the accepted MOOC practice of short segments, although we did not always exactly meet the eight-minute upper limit that was suggested to us.

We hope that you, and many newcomers, will like the course and benefit from it.

References

[3] EdX course: Computing: Art, Magic, Science, preview available here.

[2] Bertrand Meyer: Touch of Class: Learning how to Program Well, with Objects and Contracts, Springer Verlag, revised printing, 2013, book page here.

[3] Learning to Program, Online: article from this blog, 3 February 2014, available here.

 

 

When pictures lie

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One of the most improvable characteristics of scientific papers is the graphical presentation of numerical data. It is sad to see that thirty years after Tufte published the first edition of his masterpiece [1] many authors are still including grossly inaccurate graphics. Sadder still when the authors are professional graphists, who should know better. Take this chart [2] from the last newsletter of Migros, Switzerland’s largest supermarket chain. To convince Swiss people that they should not worry about their food bills, it displays the ratio of food expenses to revenue in various countries. There would be many good ways to represent this information graphically, but someone thought it clever to draw variable-size coins of the respective currencies. According to the text, “the bigger the circle, the larger the income’s share devoted to food“.

Just a minor problem: the visual effect is utterly misleading. Taking three examples from the numbers given, the ratio is roughly 10% for Switzerland, 30% for Russia, 40% for Morocco. And, sure enough,  compared to the Swiss coin in the figure, the Russian coin is about three times bigger and the Moroccan coin four times… in diameter! What the eye sees, of course, is the area. Since the area varies as the square of the diameter, one gets the impression that Russians spend nine times, not three, and Moroccans sixteen times, not four, as much as the Swiss.

To convey the correct suggestion, the diameter of the Russian coin should have been about 73% larger than the Swiss coin’s diameter (the square root of three is about 1.73) , and the diameter of the Moroccan coin twice larger, that is to say half of what it is.

The impression is particularly misleading for countries where the ratio, unlike in Russia or Morocco, is close to Switzerland’s. Most interestingly, although no doubt by accident, for neighboring countries, where Swiss people are prone to go shopping in search of a bargain, a practice that possibly does not enthuse Migros. The extra percentage devoted to food (using this time  no longer rough approximations but precise values from the figures given in the Migros page) is 4% for Austria (10.9 ratio vs Switzerland’s 10.2), 8% for Germany (11.1), 30% for France (13.3) and  43% for Italy (14.6). But if you look at the picture the circles suggest much bigger differences; for example the Italian circle is obviously computed from the ratio of the squares, 14.62 / 10.62, showing an increase of 104%. In other words, Italians proportionally devote to food a little over two-fifths more  than the Swiss, but the graph suggests they spend twice as much.

On the premise that one should not ascribe to malevolence what can be explained by ignorance, I hope the Migros graphists will get a copy of Tufte’s book for their future endeavors.

Read Tufte too if you want the pictures in your papers to be not just attractive but accurate.

References

[1] Edward R. Tufte: The Visual Display of Quantitative Information, Graphics Press, second edition, 2001. See his site here.

[2] “How much do we spend to feed ourselves?” on the Migros site, available here for the French version (replace “fr” in the URL by “de” for German and “it” for Italian, I did not see an English version). Click on the figure for a readable version.

The Eiffel Documentation Drive

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EiffelStudio releases are semi-annual, end of May and end of November. Release 14-05 just came out. The next release (14-11) is entirely devoted to documentation. We are hoping for extensive community involvement in this first-time Eiffel Documentation Drive.

Many people regularly comment that there is not enough Eiffel and EiffelStudio documentation, and some of what exists is not good enough. We have decided to tackle the problem seriously, hence the dedication of an entire release cycle to documentation. The term is taken here in a broad sense: “documentation” means what is at http://docs.eiffel.com, but also everything else that can help understand Eiffel, for example updating Wikipedia entries on topics for which Eiffel has something to offer.

Anyone with an understanding of an Eiffel-related topic can help. We particularly need help from two (non-disjoint) categories of contributors

  • Those with a good understanding of one or more Eiffel-related topics.
  • Those with good writing skills.

The process will involve reviewing, so if you are an Eiffelist with moderate taste for writing, or a good writer with incomplete knowledge of Eiffel, we need your help anyway; someone else will compensate for the missing side. In particular, a common criticism is that some of the documentation was written by developers who do not have English as their mother tongue; if you can help improve it everyone will benefit. Of course if you are good at both technology and writing it’s even better.

We are mentioning English because it is the first target, but documentation in other languages, either original or a translation of existing English pages, is needed too.

Here is how the Eiffel Documentation Drive works:

  • Here you will find a form to report missing or unsatisfactory documentation. Please fill it on every applicable occasion.
  • The entries will be read by a member of the Eiffel Software team, who in applicable cases will add a row to the Eiffel Documentation Drive spreadsheet here. You can not only read that spreadsheet but also edit it yourself, so as to keep it as accurate and up-to-date as possible.
  • An email will be sent to the user list, with “Eiffel Documentation Drive” in the header (so that people not interested in the topic can filter them out), requesting help.
  • Those willing to help can enter their names in the corresponding row, indicating a planned date of completion.

Each row includes among its fields the following: topic, link to existing documentation, volunteer writer(s), planned completion, volunteer reviewer(s).

The full Eiffel Software team will participate – as noted above, improving the documentation is the strategic goal for the release – but we hope for considerable community participation. Please help make EiffelStudio documentation shine as much as the environment itself.

Lamport, Turing

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Theories abound (I have my own) about why it did not happen long ago, but at last Lamport did receive the Turing Award. For me what most characterizes him is a stream [1] of elegant, original, insightful articles providing solutions to one important and thorny problem after another. Some of these articles are well known but many gems are not; see for example his take on Buridan’s Ass [2], not even a computer science paper, offering a convincing treatment of a centuries-old riddle.

References

[1] Leslie Lamport: annotated publication list, here.

[2] Leslie Lamport: Buridan’s Principle, “to appear in Foundations of Physics“, dated 24 February 2012, available here.

Eiffel as an expression language

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A functional-programming style, or more generally a style involving more expressions and fewer instructions, is possible in Eiffel. In particular, Eiffel’s agent mechanism embeds a full functional-programming mechanism in the object-oriented framework of the language.

To make the notations simpler, we are discussing and tentatively implementing a number of proposed extensions. They involve no fundamental new language mechanisms, but provide new, more concise notations for existing mechanisms. Examples are:

  • Conditional expressions.
  • Implicit tuple, a rule allowing the omission of brackets for an actual argument when it is a tuple and the last argument, e.g. f (x, y, z) as an abbreviation for f ([x, y, z]) (an example involving just one argument). Tuples already provided the equivalent of a variable-argument (“varargs”) facility, but it is made simpler to use with this convention.
  • Parenthesis alias, making it possible to write just f (x, y) when f is an agent (closure, lambda expression, delegate etc. in other terminologies), i.e. treating f as if it were a function; the notation is simply an abbreviation for f.item ([x, y]) (an example that also takes advantage of implicit tuples). It has many other applications since a “parenthesis alias” can be defined for a feature of any class.
  • Avoiding explicit assignments to Result.
  • Type inference (to avoid explicitly specifying the type when it can be deduced from the context). This is a facility for the programmer, useful in particular for local variables, but does not affect the type system: Eiffel remains strongly typed, it is just that you can be lazy about writing the type when there is no ambiguity.
  • In the same vein, omitting the entire list of generic parameters when it can be inferred.

The description of the mechanism (see the link in [1]) is in the form of a set of slides explaining the concepts and presenting example. This is a working document and feedback is welcome.

References

[1] Eiffel as an expression language, Eiffel Software working document, 2012-2014, see here.

Informatics education in Europe: Just the facts

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In 2005 a number of us started Informatics Europe [1], the association of university departments and industrial research labs in computer science in Europe. The association has now grown to 80 members across the entire continent; it organizes the annual European Computer Science Summit and has published a number of influential reports. The last one just came out: Informatics Education in Europe: Institutions, Degrees, Students, Positions, Salaries — Key Data 2008-2012 [2]. The principal author is Cristina Pereira, who collected and organized the relevant data over more than a year; I helped with the preparation of the final text.

At the beginning of Informatics Europe we considered with particular attention  the model of the Computing Research Association [3], which played a crucial role in giving computer science (informatics) its due place in the US academic landscape. Several past and current officers of the CRA,  such as Willy Zwaenepoel, Ed Lazowska, Bob Constable, Andy Bernat, Jeannette Wing, Moshe Vardi and J Strother Moore gave keynotes at our early conferences and we of course asked them for the secrets of their organization’s success. One answer that struck us was the central role played by data collection. Just gathering the  facts, such as degrees and salaries, established for the first time a solid basis for serious discussions. We took this advice to heart and the report is the first result.

Gathering the information is particularly difficult for Europe given the national variations and the absence of centralized statistical data. Even the list of names under which institutions teach informatics in Europe fills a large table in the report. Cristina’s decision was, from the start, to favor quality over quantity: to focus on impeccable data for countries for which we could get it, rather than trying to cover the whole continent with data of variable credibility.

The result is the first systematic repository of basic information on informatics education in Europe: institutions, degrees offered and numbers awarded, student numbers, position titles and definitions, and (a section which will not please everyone) salaries for PhD students, postdocs and professors of various ranks.

The report is a first step; it only makes sense if we can regularly continue to update it and particularly extend it to other countries. But even in its current form (and with the obvious observation that my opinion is not neutral)  I see it as a major step forward for the discipline in Europe. We need an impeccable factual basis to convince the public at large and political decision-makers to give informatics the place it deserves in today’s educational systems.

References

[1] Informatics Europe site, see here.

[2] Cristina Pereira and Bertrand Meyer: Informatics Education in Europe: Institutions, Degrees, Students, Positions, Salaries — Key Data 2008-2012, Informatics Europe report, 30 September 2013, available here.

[3] Computing Research Association (US), see here.

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The secret of success

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In the process of finishing a book right now, it occurred to me that writing is really a simple matter. There are only three issues to address:

  1. How to start.
  2. How to finish.
  3. How to take care of the stuff in-between.

In the early stages  (1) you face the anguish of the “empty paper, defended by its whiteness” (Mallarmé) and do not know where to begin. If you overcome it, you have all the grunt work to do (3), step after step. At the end (2), while deep down you feel you have done enough and should be permitted to  click “Publish”, you still have to fill the holes, which may be small in number but have remained holes for a reason: you have again and again delayed filling them because in reality you do not know what to write there.

All things considered, then, it is not such a big deal.

Reading notes: misclassified bugs

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(Please note the general disclaimer [1].)

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

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

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

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

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

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

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

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

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

Notes and references

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

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

Presentations at ICSE and VSTTE

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

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

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