Archive for the ‘Education’ Category.

Ten traits of exceptional innovators

Imagine having had coffee, over the years, with each of Euclid, Galileo, Descartes, Newton, Einstein, Planck and de Broglie. For a computer scientist, if we set aside the founding generation (the Turings and von Neumanns), the equivalent is possible. I have had the privilege of meeting and in some cases closely interacting with pioneer scientists, technologists and entrepreneurs, including Nobel, Fields and Turing winners, Silicon-Valley-type founders and such. It is only fair that I should share some of the traits I have observed in them.

Clarification and disclaimer:

  • This discussion is abstract and as a result probably boring because I am not citing anyone by name (apart from a few famous figures, most of whom are dead and none of whom I have met). It would be more concrete and lively if I buttressed my generalities by actual examples, of which I have many. The absence of any name-dropping is a matter of courtesy and respect for people who have interacted with me unguardedly as a colleague, not a journalist preparing a tell-all book. I could of course cite the names for positive anecdotes only, but that would bias the story (see point 4). So, sorry, no names (and I won’t relent even if you ask me privately — mumm like a fish).
  • I am looking at truly exceptional people. They are drawn from a more general pool of brilliant, successful scientists and technologists, of which they form only a small subset. Many of their traits also apply to this more general community and to highly successful people in any profession. What interests me is the extra step from brilliant to exceptional. It would not be that difficult to identify fifty outstanding mathematics researchers in, say, 1900, and analyze their psychological traits. The question is: why are some of them Hilbert and Poincaré, and others not?
  • Of course I do not even begin to answer that question. I only offer a few personal remarks.
  • More generally, cargo cult does not work. Emulating every one of the traits listed below will not get you a Nobel prize. You will not turn into a great composer by eating lots of Tournedos Rossini. (Well, you might start looking like the aging Rossini.) This note presents some evidence; it does not present any conclusion, let alone advice. Any consequence is for you to draw, or not.
  • The traits obviously do not universally characterize the population observed. Not all of the people exhibit all of the traits. On the other hand, my impression is that most exhibit most.

1 Idiosyncratic

“Idiosyncratic” is a high-sounding synonym for “diverse,” used here to deflect the ridicule of starting a list of what is common to those people by stating that they are different from each other. The point is important, though, and reassuring. Those people come in all stripes, from the stuffy professor to the sandals-shorts-and-Hawaiian-shirt surfer.  Their ethnic backgrounds vary. And (glad you asked) some are men and some are women.

Consideration of many personality and lifestyle features yields not pattern at all. Some of the people observed are courteous, a delight to deal with, but there are a few jerks too. Some are voluble, some reserved. Some boastful, some modest. Some remain for their full life married to the same person, some have been divorced many times, some are single. Some become CEOs and university presidents, others prefer the quieter life of a pure researcher. Some covet honors, others are mostly driven by the pursuit of knowledge. Some wanted to become very rich and did, others care little about money.  It is amazing to see how many traits appear irrelevant, perhaps reinforcing the value of those that do make a difference.

2 Lucky

In trying to apply a cargo-cult-like recipe, this one would be the hardest to emulate. We all know that Fleming came across penicillin thanks to a petri dish left uncleaned on the window sill; we also know that luck favors only the well-prepared: someone other than Fleming would have grumbled at the dirtiness of the place and thrown the dish into the sink. But I am not just talking about that kind of luck. You have to be at the right place at the right time.

Read the biographies, and you will see that almost always the person happened to study with a professor who just then was struggling with a new problem, or did an internship in a group that had just invented a novel technique, or heard about recent results before everyone else did.

Part of what comes under “luck” is luck in obtaining the right education. Sure, there are a few autodidacts, but most of the top achievers studied in excellent institutions.

Success comes from a combination of nature and nurture. The perfect environment, such as a thriving laboratory or world-class research university, is not enough; but neither is individual brilliance. In most cases it is their combination that produces the catalysis.

3 Smart

Laugh again if you wish, but I do not just mean the obvious observation that those people were clever in what they did. In my experience they are extremely intelligent in other ways too. They often possess deep knowledge beyond their specialties and have interesting conversations.

You approach them because of the fame they gained in one domain, and learn from them about topics far beyond it.

4 Human

At first, the title of this section is another cause for ridicule: what did you expect, extraterrestrials? But “human” here means human in their foibles too. You might expect, if not an extraterrestrial, someone of the oracle-of-Delphi or wizard-on-a-mountain type, who after a half-hour of silence makes a single statement perfect in its concision and exactitude.

Well, no. They are smart, but they say foolish things too. And wrong things. Not only do they say them, they even publish them. (Newton wasted his brilliance on alchemy. Voltaire — who was not a scientist but helped promote science, translating Newton and supporting the work of Madame du Châtelet — wasted his powerful wit to mock the nascent study of paleontology: so-called fossils are just shells left over by picnicking tourists! More recently, a very famous computer scientist wrote a very silly book — of which I once wrote, fearlessly, a very short and very disparaging review.)

So what? It is the conclusion of the discussion that counts, not the meanderous path to it, or the occasional hapless excursion into a field where your wisdom fails you. Once you have succeeded, no one will care how many wrong comments you made in the process.

It is fair to note that the people under consideration probably say fewer stupid things than most. (The Erich Kästner ditty from an earlier article applies.) But no human, reassuringly perhaps, is right 100% of the time.

What does set them apart from many people, and takes us back to the previous trait (smart), is that even those who are otherwise vain have no qualms recognizing  mistakes in their previous thinking. They accept the evidence and move on.

5 Diligent

Of two people, one an excellent, top-ranked academic, the other a world-famous pioneer, who is the more likely to answer an email? In my experience, the latter.

Beyond the folk vision of the disheveled, disorganized, absent-minded professor lies the reality of a lifetime of rigor and discipline.

This should not be a surprise. There is inspiration, and there is perspiration.  Think of it as the dual of the  broken-windows theory, or of the judicial view that a defendant who lies in small things probably lies in big things: the other way around, if you do huge tasks well, you probably do small tasks well too.

6 Focused

Along with diligence comes focus, carried over from big matters to small matters. It is the lesser minds that pretend to multiplex. Great scientists, in my experience, do not hack away at their laptops during talks, and they turn off their cellphones. They choose carefully what they do (they are deluged with requests and learn early to say no), but what they accept to do they do. Seriously, attentively, with focus.

A fascinating spectacle is a world-famous guru sitting in the first row at a conference presentation by a beginning Ph.D. student, and taking detailed notes. Or visiting an industrial lab and quizzing a junior engineer about the details of the latest technology.

For someone who in spite of the cargo cult risk is looking for one behavior to clone, this would be it. Study after study has shown that we only delude ourselves in thinking we can multiplex. Top performers understand this. In the seminar room, they are not the ones doing email. If they are there at all, then watch and listen.

7 Eloquent

Top science and technology achievers are communicators. In writing, in speaking, often in both.

This quality is independent from their personal behavior, which can cover the full range from shy to boisterous.  It is the quality of being articulate. They know how to convey their results — and often do not mind crossing the line to self-advertising. It is not automatically the case that true value will out: even the most impressive advances need to be pushed to the world.

The alternative is to become Gregor Mendel: he single-handedly discovered the laws of genetics, and was so busy observing the beans in his garden that no one heard about his work until some twenty years after his death. Most people prefer to get the recognition earlier. (Mendel was a monk, so maybe he believed in an afterlife; yet again maybe he, like everyone else, might have enjoyed attracting interest on this earth first.)

In computer science it is not surprising that many of the names that stand out are of people who have written seminal books that are a pleasure to read. Many of them are outstanding teachers and speakers as well.

8 Open

Being an excellent communicator does not mean that you insist on talking. The great innovators are excellent listeners too.

Some people keep talking about themselves. They exist in all human groups, but this particular trait is common among scientists, particularly junior scientists, who corner you and cannot stop telling you about their ideas and accomplishments. That phenomenon is understandable, and in part justified by an urge to avoid the Mendel syndrome. But in a conversation involving some less and some more recognized professionals it is often the most accomplished members of the group who talk least. They are eager to learn. They never forget that the greatest insighs can start with a casual observation from an improbable source. They know when to talk, and when to shut up and listen.

Openness also means intellectual curiosity, willingness to have your intellectual certainties challenged, focus on the merit of a comment rather than the commenter’s social or academic status, and readiness to learn from disciplines other than your own.

9 Selfish

People having achieved exceptional results were generally obsessed with the chase and the prey. They are as driven as an icebreaker ship in the Sea of Barents. They have to get through; the end justifies the means; anything in the way is collateral damage.

So it is not surprising, in the case of academics, to hear colleagues from their institutions mumble that X never wanted to do his share, leaving it to others to sit in committees, teach C++ to biology majors and take their turn as department chair. There are notable exceptions, such as the computer architecture pioneer who became provost then president at Stanford before receiving the Turing Award. But  you do not achieve breakthroughs by doing what everything else is doing. When the rest of the crowd is being sociable and chatty at the conference party long into the night, you go back to your hotel to be alert for tomorrow’s session. A famous if extreme case is Andrew Wiles, whom colleagues in the department considered a has-been, while he was doing the minimum necessary to avoid trouble while working secretly and obsessively to prove Fermat’s last theorem.

This trait is interesting in light of the soothing discourse in vogue today. Nothing wrong with work-life balance, escaping the rat race, perhaps even changing your research topic every decade (apparently the rule in some research organizations). Sometimes a hands-off, zen-like attitude will succeed where too much obstination would get stuck. But let us not fool ourselves: the great innovators never let go of the target.

10. Generous

Yes, selfishness can go with generosity. You obsess over your goals, but it does not mean you forget other people.

Indeed, while there are a few solo artists in the group under observation, a striking feature of the majority is that in addition to their own achievements they led to the creation of entire communities, which often look up to them as gurus. (When I took the comprehensive exam at Stanford, the first question was what the middle initial “E.” of a famous professor stood for. It was a joke question, counting for maybe one point out of a hundred, helpfully meant to defuse students’ tension in preparation for the hard questions that followed. But what I remember is that every fellow student whom I asked afterwards knew the answer. Me too. Such was the personality cult.) The guru effect can lead to funny consequences, as with the famous computer scientist whose disciples you could spot right away in conferences by their sandals and beards (I do not remember how the women coped), carefully patterned after the master’s.

The leader is often good at giving every member of that community flattering personal attention. In a retirement symposium for a famous professor, almost every person I talked too was proud of having developed a long-running, highly personal and of course unique relationship with the honoree. One prestigious computer scientist who died in the 80’s encouraged and supported countless young people in his country; 30 years later, you keep running into academics, engineers and managers who tell you that they owe their career to him.

Some of this community-building can be self-serving and part of a personal strategy for success. There has to be more to it, however. It is not just that community-building will occur naturally as people discover the new ideas: since these ideas are often controversial at first, those who understood their value early band together to defend them and support their inventor. But there is something else as well in my observation: the creators’ sheer, disinterested generosity.

These people are passionate in their quest for discovery and creation and genuinely want to help others. Driven and self-promoting they may be, but the very qualities that led to their achievements — insight, intellectual courage, ability to think beyond accepted ideas — are at the antipodes of pettiness and narrow-mindedness. A world leader cannot expect any significant personal gain from spotting and encouraging a promising undergraduate, telling a first-time conference presenter that her idea is great and worth pushing further, patiently explaining elementary issues to a beginning student, or responding to a unknown correspondent’s emails. And still, as I have observed many times, they do all of this and more, because they are in the business of advancing knowledge.

These are some of the traits I have observed. Maybe there are more but, sorry, I have to go now. The pan is sizzling and I don’t like my tournedos too well-done.

recycled-logo (Originally published on CACM blog.)

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Festina retro

We “core” computer scientists and software engineers always whine that our research themes forever prevent us, to the delight of our physicist colleagues but unjustly, from reaching the gold standard of academic recognition: publishing in Nature. I think I have broken this barrier now by disproving the old, dusty laws of physics! Brace yourself for my momentous discovery: I have evidence of negative speeds.

My experimental setup (as a newly self-anointed natural scientist I am keen to offer the possibility of replication) is the Firefox browser. I was downloading an add-on, with a slow connection, and at some point got this in the project bar:

Negative download speed

Negative speed! Questioning accepted wisdom! Nobel in sight! What next, cold fusion?

I fear I have to temper my enthusiasm in deference to more mundane explanations. There’s the conspiracy explanation: the speed is truly negative (more correctly, it is a “velocity”, a vector of arbitrary direction, hence in dimension 1 possibly negative); Firefox had just reversed the direction of transfer, surreptitiously dumping my disk drive to some spy agency’s server.

OK, that is rather far-fetched. More likely, it is a plain bug. A transfer speed cannot be negative; this property is not just wishful thinking but should be expressed as an integral part of the software. Maybe someone should tell Firefox programmers about class invariants.

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Before I start screaming once again…

… at my would-be coauthors, would someone please tell them, and every non-native-English-speaker-but-aspiring-English-author, to read this? Please, please, please, please, please.

In English the verb “allow” cannot take an infinitive as a complement. Ever. You may not write “my method allows to improve productivity” (even if it’s true, which it probably isn’t, but never mind). Ever. You may write the equivalent in French, German, Russian, Italian and whatever, but not in English. Ever. In English you do not “allow to” do something. Ever. You allow someone or something to do something. Maybe, or maybe not, your method allows its users to improve productivity. That’s correct English. It is also OK to use a gerund [1]: your method allows improving productivity. Actually that sounds clumsy but at least it is grammatically correct.

The reason the gerund does not sound quite right here is that  in situations where foreign speakers instinctively think “allow to…” in their mother tongues and transport it directly to English, the native English speaker instinctively comes up with  something  different. Typically, one of:

  • Allow someone to, using a specific word instead of “someone”. The English language has a concrete slant and favors expressing all details, including some that in other languages remain implicit.
  • Make it possible to:  a bit wordy, but common and convenient, and definitely correct when followed by an infinitive (“my method makes it possible to improve productivity”). We politely leave it unsaid what the “it” is that is being made possible. This turn of phrase is the easiest if you want to remain as close to the original “allow to…” in your native language. Consider “make it possible to” as a mechanical translation of “allow to”. It works.
  • Support something. Remember this word. It is used more widely in English than its typical translations in other languages. Often it fits just where you initially would come up with “allow to”. Your method may support policies for improving productivity.
  • The gerund. It will sound less clumsy if what you are “allowing” is truly a process, and you are using “allow” in its direct sense of giving permission [2], rather than in the more general and weaker sense of supporting. The rules of tennis allow playing in either singles or doubles.
  • Generalizing the gerund, a plain noun (substantive). You can, in fact, allow something. Your methodology allows productivity improvements. Like the gerund, it does not sound as good as the other forms (“support” is better unless there truly is a notion of permission), but it is correct.
  • Or… nothing at all. Paraphrased from a text seen recently: “some techniques only allow to model internal properties, others allow to model external properties too”. So much better (in any language): some techniques only model internal properties, others also cover external ones. Whoever wrote the first variant should not, in the next three years, be allowed anywhere near the word “allow”.

Some people go around the issue by using “allow for doing something”. That usage is acceptable in American English (less so in British English), but by default “allow for” means something else: tolerating some possible variation in an estimate, as in “plan two hours for your drive, allowing for traffic”. As a substitute for “allowing to” this phrase has no advantage over the solutions listed above.

On last count, I had corrected “allow to” in drafts from coworkers, using one of these solutions, approximately 5,843,944,027 times (allowing for a few cases that I might have forgotten). Enough! Please, please, please, please, please, please, please. Make a note of this. It will allow me to live better, it will allow you to avoid my wrath, it  will make it possible for us to work together again, it will support a better understanding among the people in the world, it will allow faster refereeing and a better peer review process, it covers all needs, and it still allows for human imperfection.

Notes

[1] Or gerundive, or present participle: a word form resulting from addition of the suffix “-ing” to a verb radical.

[2] Note that beyond “allow” this discussion also applies to the verb “permit”. You permit someone to do something.

[3] Post-publication, Oscar Nierstrasz mentioned on Facebook that he has a Web page addressing the same point.

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Small and big pleasures

(Reproduced from my CACM blog.)

One of the small pleasures of life is to win a technical argument with a graduate student. You feel good, as well you should. It is only human to be want to be right. Besides, if you ended up being wrong all or most of the time, you should start questioning your sanity: why are they the students and you the supervisor, rather than the other way around?

One of the big pleasures of life is to lose an argument with a graduate student. Then you have learned something.

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Devops (the concept, and a workshop announcement)

One of the most significant recent developments in software engineering is the concept of Devops*. Dismissing the idea as “just the latest buzzword” would be wrong. It may be a buzzword but it reflects a fundamental change in the way we structure system development; with web applications in particular the traditional distinctions between steps of development, V&V** and deployment fade out. If you are using Microsoft Word, you know or can easily find out the version number; but which version of your search engine are you using?

With the new flexibility indeed come new risks, as when a bug in the latest “devopsed”  version of Google Docs caused me to lose a whole set of complex diagrams irretrievably; an earlier article on this blog (“The Cloud and Its Risks“, October 2010) told the story.

In the new world of continuous integrated development/V&V/deployment, software engineering principles are more necessary than ever, but their application has to undergo a profound adaptation.

With Jean-Michel Bruel (Toulouse), Elisabetta Di Nitto (Milan) and Manuel Mazzara (Innopolis), we are organizing a workshop on the topic, DEVOPS 18, on 5-6 March 2018 near Toulouse. The Call for Papers is available here, with Springer LNCS proceedings. The submission deadline is January 15, but for that date a 2-page extended abstract is sufficient. I hope that the event will help the community get a better grasp of the software engineering techniques and practices applicable to this new world of software development.

Notes

*I know, it’s supposed to be DevOps (I am not a great fan of upper case in the middle of words).
** Validation & Verification.

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Split the Root: a little design pattern

Many programs take “execution arguments” which the program users provide at the start of execution. In EiffelStudio you can enter them under Execution -> Execution parameters.

The program can access them through the Kernel Library class ARGUMENTS. Typically, the root class of the system inherits from ARGUMENTS and its creation procedure will include something like

if argument_count /= N then
……..print (“XX expects exactly N arguments: AA, BB, …%N”)
else
……..u := argument (1) ; v := argument (2) ; …
……..“Proceed with normal execution, using u, v, …”
end

where N is the number of expected arguments, XX is the name of the program, and AA, …. are the roles of arguments. u, v, … are local variables. The criterion for acceptance could be “at least N” instead of exactly N. The features argument_count and arguments come from class ARGUMENTS.

In all but trivial cases this scheme (which was OK years ago, in a less sophisticated state of the language) does not work! The reason is that the error branch will fail to initialize attributes. Typically, the “Proceed with…” part in the other branch is of the form

               attr1 := u
                attr2 := v
                …
                create obj1.make (attr1, …)
                create obj2.make (attr2, …)
                “Work with obj1, obj2, …”

If you try to compile code of this kind, you will get a compilation error:

Compiler error message

Eiffel is void-safe: it guarantees that no execution will ever produce null-pointer dereference (void call). To achieve this guarantee, the compiler must make sure that all attributes are “properly set” to an object reference (non-void) at the end of the creation procedure. But the error branch fails to initialize obj1 etc.

You might think of replacing the explicit test by a precondition to the creation procedure:

               require
                                argument_count = N

but that does not work; the language definition explicit prohibits preconditions in a root creation procedure. The Ecma-ISO standard (the official definition of the language, available here) explains the reason for the corresponding validity rule (VSRP, page 32):

A routine can impose preconditions on its callers if these callers are other routines; but it makes no sense to impose a precondition on the external agent (person, hardware device, other program…) that triggers an entire system execution, since there is no way to ascertain that such an agent, beyond the system’s control, will observe the precondition.

The solution is to separate the processing of arguments from the rest of the program’s work. Add a class CORE which represents the real core of the application and separate it from the root class, say APPLICATION. In APPLICATION, all the creation procedure does is to check the arguments and, if they are fine, pass them on to an instance of the core class:

                note
                                description: “Root class, processes execution arguments and starts execution”
                class APPLICATION create make feature
                                core: CORE
                                                — Application’s core object
                                make
……..……..……..……..……..……..— Check arguments and proceed if they make sense.
                                                do
                                                             if argument_count /= N then
                                                                                print (“XX expects exactly N arguments: AA, BB, …%N”)
                                                                else
                                                                                create core.make (argument (1), argument (2) ; …)
                                                                                                — By construction the arguments are defined!
                                                                                core.live
                                                                                                — Perform actual work
                                                                                               — (`live’ can instead be integrated with `make’ in CORE.)

                                                                end
                                                end
                 end
 
We may call this little design pattern “Split the Root”. Nothing earth-shattering; it is simply a matter of separating concerns (cutting off the Model from the View). It assumes a system that includes text-based output, whereas many applications are graphical. It is still worth documenting, for two reasons.

First, in its own modest way, the pattern is useful for simple programs; beginners, in particular, may not immediately understand why the seemingly natural way of processing and checking arguments gets rejected by the compiler.

The second reason is that Split the Root illustrates the rules that preside over a carefully designed language meant for carefully designed software. At first it may be surprising and even irritating to see code rejected because, in a first attempt, the system’s root procedure has a precondition, and in a second attempt because some attributes are not initialized — in the branch where they do not need to be initialized. But there is a reason for these rules, and once you understand them you end up writing more solid software.

 

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New session of online Agile course starts now

Just about a year ago I posted this announcement about my just released Agile course:

In spite of all the interest in both agile methods and MOOCs (Massive Open Online Courses) there are few courses on agile methods; I know only of some specialized MOOCs focused on a particular language or method.

I produced for EdX, with the help of Marco Piccioni, a new MOOC entitled Agile Software Development. It starts airing today and is supported by exercises and quizzes. The course uses some of the material from my Agile book.

The course is running again! You can find it on EdX here.

Such online courses truly “run”: they are not just canned videos but include exercises and working material on which you can get feedback.

Like the book (“Agile: The Good, the Hype and the Ugly“, Springer), the course is a tutorial on agile methods, presenting an unbiased analysis of their benefits and limits.

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LASER summer school on software for robotics: last call for registration

Much of the progress in robotics is due to software advances, and software issues remain at the heart of the formidable challenges that remain. The 2017 LASER summer school, held in September in Elba, brings together some of the most prestigious international experts in the area.

The LASER school has established itself as one of the principal forums to discussed advanced software issues. The 2017 school takes place from 9 to 17 September in the idyllic setting of the Hotel del Golfo in Procchio, Elba Island, Italy.

Robotics is progressing at an amazing pace, bringing improvements to almost areas of human activity. Today’s robotics systems rely ever more fundamentally on complex software, raising difficult issues. The LASER 2017 summer school covers both the current state of robotics software technology and open problems. The lecturers are top international experts with both theoretical contributions and major practical achievements in developing robotics systems.
The LASER school is intended for professionals from the industry (engineers and managers) as well as university researchers, including PhD students. Participants learn about the most important software technology advances from the pioneers in the field. The school’s focus is applied, although theory is welcome to establish solid foundations. The format of the school favors extensive interaction between participants and speakers.

We have lined up an impressive roster of speakers from the leading edge of both industry and academia:

Rodolphe Gélin, Aldebaran Robotics
Ashish Kapoor, Microsoft Research
Davide Brugali, University of Bergamo, on Managing software variability in robotic control systems
Nenad Medvidovic, University of Southern California, on Software Architectures of Robotics Systems
Bertrand Meyer, Politecnico di Milano & Innopolis University, on Concurrent Object-Oriented Robotics Software
Issa Nesnas, NASA Jet Propulsion Laboratory, on Experiences from robotic software development for research and planetary flight robots
Hiroshi (“Gitchang”) Okuno, Waseda University & Kyoto University, on Open-Sourced Robot Audition Software HARK: Capabilities and Applications

The school takes place at the magnificent Hotel del Golfo in the Gulf of Procchio, Elba. Along with an intensive scientific program, participants will have time to enjoy the countless natural and cultural riches of this wonderful, history-laden jewel of the Mediterranean.

For more information about the school, the speakers and registration see the LASER site.

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Agile MOOC starts this week

In spite of all the interest in both agile methods and MOOCs (Massive Open Online Courses) there are few courses on agile methods; I know only of some specialized MOOCs focused on a particular language or method.

I produced for EdX, with the help of Marco Piccioni, a new MOOC entitled Agile Software Development. It starts airing today and is supported by exercises and quizzes. The course uses some of the material from my Agile book.

Registration is free and open to anyone at this address.

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Software for Robotics: 2016 LASER summer school, 10-18 September, Elba

The 2016 session of the LASER summer school, now in its 13th edition, has just been announced. The theme is new for the school, and timely: software for robotics. Below is the announcement.

School site: here

The 2016 LASER summer school will be devoted to Software for Robotics. It takes place from 10 to 18 September in the magnificent setting of the Hotel del Golfo in Procchio, Elba Island, Italy.

Robotics is progressing at an amazing pace, bringing improvements to almost areas of human activity. Today’s robotics systems rely ever more fundamentally on complex software, raising difficult issues. The LASER 2016 summer school both covers the current state of robotics software technology and open problems. The lecturers are top international experts with both theoretical contributions and major practical achievements in developing robotics systems.
The LASER school is intended for professionals from the industry (engineers and managers) as well as university researchers, including PhD students. Participants learn about the most important software technology advances from the pioneers in the field. The school’s focus is applied, although theory is welcome to establish solid foundations. The format of the school favors extensive interaction between participants and speakers.
The speakers include:

  • Joydeep Biswas, University of Massachussetts, on Development, debugging, and maintenance of deployed robots
  • Davide Brugali, University of Bergamo, on Managing software variability in robotic control systems
  • Nenad Medvidovic, University of Southern California, on Software Architectures of Robotics Systems
  • Bertrand Meyer, Politecnico di Milano and Innopolis University, with Jiwon Shin, on Concurrent Object-Oriented Robotics Software: Concepts, Framework and Applications
  • Issa Nesnas, NASA Jet Propulsion Laboratory, on Experiences from robotic software development for research and planetary flight robots
  • Richard Vaughan, Simon Fraser University

Organized by Politecnico di Milano, the school takes place at the magnificent Hotel del Golfo (http://www.hoteldelgolfo.it/) in Golfo di Procchio, Elba. Along with an intensive scientific program, participants will have time to enjoy the natural and cultural riches of this history-laden jewel of the Mediterranean.

For more information about the school, the speakers and registration see here.

.

— Bertrand Meyer

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How to learn languages

Most people in technology, trade, research or education work in an international environment and need to use a foreign language which they learned at some earlier stage [1]. It is striking to see how awfully most of us perform. International conferences are a particular pain; many speakers are impossible to understand. You just want to go home and read the paper — or, often, not.

Teachers — English teachers in the case of the most commonly used international language — often get the blame, as in “They teach us Shakespeare’s English instead of what we need for today’s life“, but such complaints are  unfounded: look at any contemporary language textbook and you will see that it is all about some Svetlanas or Ulriches or Natsukos meeting or tweeting their friends Cathy and Bill.

It is true, though, that everyone teaches languages the wrong way.

There is only one way to teach languages right: start with the phonetics. Languages were spoken [2] millennia before they ever got written down. The basis of all natural languages is vocal. If you do not pronounce a language right you do not speak that language. It is unconscionable for example that most of us non-native speakers, when using English, still have an accent. We should have got rid of its last traces by age 12.

I cannot understand why people who are otherwise at the vanguard of intellectual achievement make a mess of their verbal expression, seemingly not even realizing there might be a problem. Some mistakes seem to be handed out from generation to generation. Most French speakers of English, for example, pronounce the “ow” of “allow” as in “low”, not “cow” (it took a long time before a compassionate colleague finally rid me of that particular mistake, and I don’t know how many more I may still be making); Italians seem to have a particular fondness for pronouncing as a “v” the “w” of “write”; an so on.

The only place I ever saw that taught languages right was a Soviet school for interpreters. Graduating students of French, having had no exposure to the language before their studies, spoke it like someone coming out of a Métro station. (Actually they spoke more like a grandmother coming out of the Métro, since they had little access to contemporary materials, but that would have been easy to fix.) The trick: they spent their entire first year doing phonetics, getting the “r”and the “u” and so on right, shedding the intonation of their native tongue. That year was solely devoted to audio practice in a phonetics lab. At the end of it they did not know the meaning of what they were saying,but they said it perfectly. Then came a year of grammar, then a year of conversation. Then came the Métro result. (This is not an apology of the Soviet Union. Someone there just happened to get that particular thing right.)

We should teach everyone this way. There is no reason to tolerate phonetic deviations. If you do not get the sounds exactly as they should be, everything else will be flawed. Take, for example, the “r”. If, like me, you cannot roll your “r”s, then when you try to speak Russian or Italian, even if you think you can get the other sounds right you don’t because  your tongue or palate or teeth are in the wrong place. Another example is the “th” sound in English (two distinct sounds in fact) which I never got right. I can fake it but then something else comes out wrong and I still sound foreign. My high-school teachers — to whom I owe gratitude for so much else — should have tortured me until my “th”s were perfect. True, teaching time is a fixed-pie problem, but I am sure something else could have been sacrificed. Since, for example, I can answer in a blink that seven times nine is sixty-three, I must at some stage have taken the time to memorize it. In retrospect I would gladly sacrifice that element of knowledge, which I can reconstruct when needed, for the ability to roll my “r”s.

Age is indeed critical. While we humans can learn anything at any time, it is a well-known fact (although the reasons behind it remain mysterious) that until puberty we are malleable and can learn languages perfectly.  Witness bilingual and trilingual children; they do not have any accent. But around the time we develop new abilities and desires our brain shuts itself off to that particular skill; from then on we can only learn languages at great pain, with only remote hopes of reaching the proficiency of natives. The time to learn the phonetics of a foreign language, and learn it perfectly, is around the age of nine or ten at the latest. Then, at the age of reason, we should learn the structures — the grammar. Declensions in German, the use of tenses in English, the perfective and imperfective aspects of Russian. Conversation — Svetlana greeting Cathy — can come later if there is time left. Once you have the basics wired into your head, the rest is trivial.

Focusing children on phonetics as the crucial part of learning a language will also help them shine. Like physical appearance, verbal clarity is an enormous advantage. I must not be the only one in conferences who pays far more attention to the content of an article if the speaker has impeccable pronunciation, innate or learned. Syntax and choice of words come next. Of course substance matters; we have all heard top scientists with accents thicker than a Humvee tire and grammar thinner than a summer dress.  Everyone else needs fluency.

Conceivably, someone might object that a year of phonetic drilling is not the most amusing pastime for a 10-year-old. Without even noting that it’s not worse than having to learn to play the violin — where did we ever get the idea that learning should be fun?

As to me, like those who before they die want to get into space, visit the capitals of all countries on earth or reach the top of Mount Everest, I have my dream; it has lesser impact on the environment and depends on me, not on the help of others: just once, I’d like to roll an “r” like a Polish plumber.

Notes

[1] Many native English speakers provide the exception to this observation, since they often do not learn any foreign language beyond “Buon giorno” and “melanzane alla parmigiana”, and hence will probably not see the point of this article.

[2] And motioned. Sign language as practiced by deaf people (informally before it was codified starting from the 17th century on) is also a potential teaching start.

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Understanding and assessing Agile: free ACM webinar next Wednesday

ACM is offering this coming Wednesday a one-hour webinar entitled Agile Methods: The Good, the Hype and the Ugly. It will air on February 18 at 1 PM New York time (10 AM West Coast, 18 London, 19 Paris, see here for more cities). The event is free and the registration link is here.

The presentation is based on my recent book with an almost identical title [1]. It will be a general discussion of agile methods, analyzing both their impressive contributions to software engineering and their excesses, some of them truly damaging. It is often hard to separate the beneficial from the indifferent and the plain harmful, because most of the existing presentations are of the hagiographical kind, gushing in admiration of the sacred word. A bit of critical distance does not hurt.

As you can see from the Amazon page, the first readers (apart from a few dissenters, not a surprise for such a charged topic) have relished this unprejudiced, no-nonsense approach to the presentation of agile methods.

Another characteristic of the standard agile literature is that it exaggerates the contrast with classic software engineering. This slightly adolescent attitude is not helpful; in reality, many of the best agile ideas are the direct continuation of the best classic ideas, even when they correct or adapt them, a normal phenomenon in technology evolution. In the book I tried to re-place agile ideas in this long-term context, and the same spirit will also guide the webinar. Ideological debates are of little interest to software practitioners; what they need to know is what works and what does not.

References

[1] Bertrand Meyer, Agile! The Good, the Hype and the Ugly, Springer, 2014, see Amazon page here, publisher’s page here and my own book page here.

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Lampsort

 

In support of his view of software methodology, Leslie Lamport likes to use the example of non-recursive Quicksort. Independently of the methodological arguments, his version of the algorithm should be better known. In fact, if I were teaching “data structures and algorithms” I would consider introducing it first.

As far as I know he has not written down his version in an article, but he has presented it in lectures; see [1]. His trick is to ask the audience to give a non-recursive version of Quicksort, and of course everyone starts trying to remove the recursion, for example by making the stack explicit or looking for invertible functions in calls. But his point is that recursion is not at all fundamental in Quicksort. The recursive version is a specific implementation of a more general idea.

Lamport’s version — let us call it Lampsort —is easy to express in Eiffel. We may assume the following context:

a: ARRAY [G -> COMPARABLE]        — The array to be sorted.
pivot: INTEGER                                      —  Set by partition.
picked: INTEGER_INTERVAL            — Used by the sorting algorithm, see below.
partition (i, j: INTEGER)
……..require      — i..j is a sub-interval of the array’s legal indexes:
……..……..i < j
……..……..i >= a.lower
……..……..j <= a.upper
……..do
……..……..… Usual implementation of partition
……..ensure     — The expected effect of partition:
……..……..pivot >= i
……..……..pivot < j
……..……..a [i..j] has been reshuffled so that elements in i..pivot are less than
……..……..or equal to those in pivot+1 .. j.
……..end

We do not write the implementation of partition since the point of the present discussion is the overall algorithm. In the usual understanding, that algorithm consists of doing nothing if the array has no more than one element, otherwise performing a partition and then recursively calling itself on the two resulting intervals. The implementation can take advantage of parallelism by forking the recursive calls out to different processors. That presentation, says Lamport, describes only a possible implementation. The true Quicksort is more general. The algorithm works on a set not_sorted of integer intervals i..j such that the corresponding array slices a [i..j] are the only ones possibly not sorted; the goal of the algorithm is to make not_sorted empty, since then we know the entire array is sorted. In Eiffel we declare this set as:

not_sorted: SET [INTEGER_INTERVAL]

The algorithm initializes not_sorted to contain a single element, the entire interval; at each iteration, it removes an interval from the set, partitions it if that makes sense (i.e. the interval has more than one element), and inserts the resulting two intervals into the set. It ends when not_sorted is empty. Here it is:

……..from                                 — Initialize interval set to contain a single interval, the array’s entire index range:
……..…..create not_sorted.make_one (a.lower |..| a.upper)….         ..……..
……..invariant
……..…..— See below
……..until
……..…..not_sorted.is_empty                                                            — Stop when there are no more intervals in set
……..loop
……..…..picked := not_sorted.item                                                     — Pick an interval from (non-empty) interval set.
……..……if picked.count > 1 then                                                      — (The precondition of partition holds, see below.)
……..……..…..partition (picked.lower, picked.upper)                 — Split, moving small items before & large ones after pivot.
……..……..…..not_sorted.extend (picked.lower |..| pivot)            — Insert new intervals into the set of intervals: first
……..……....not_sorted.extend (pivot + 1 |..| picked.upper)     — and second.
……..……end
……..…...not_sorted.remove (picked)                                               — Remove interval that was just partitioned.
…….end

Eiffel note: the function yielding an integer interval is declared in the library class INTEGER using the operator |..| (rather than just  ..).

The query item from SET, with the precondition not is_empty,  returns an element of the set. It does not matter which element. In accordance with the Command-Query Separation principle, calling item does not modify the set; to remove the element you have to use the command remove. The command extend adds an element to the set.

The abstract idea behind Lampsort, explaining why it works at all, is the following loop invariant (see [2] for a more general discussion of how invariants provide the basis for understanding loop algorithms). We call “slice” of an array a non-empty contiguous sub-array; for adjacent slices we may talk of concatenation; also, for slices s and t s <= t means that every element of s is less than or equal to every element of t. The invariant is:

a is the concatenation of the members of a set slices of disjoint slices, such that:
– The elements of a are a permutation of its original elements.
– The index range of any member  of slices having more than one element is in not_sorted.
– For any adjacent slices s and t (with s before t), s <= t.

The first condition (conservation of the elements modulo permutation) is a property of partition, the only operation that can modify the array. The rest of the invariant is true after initialization (from clause) with slices made of a single slice, the full array. The loop body maintains it since it either removes a one-element interval from not_sorted (slices loses the corresponding slice) or performs partition with the effect of partitioning one slice into two adjacent ones satisfying s <= t, whose intervals replace the original one in not_sorted. On exit, not_sorted is empty, so slices is a set of one-element slices, each less than or equal to the next, ensuring that the array is sorted.

The invariant also ensures that the call to partition satisfies that routine’s precondition.

The Lampsort algorithm is a simple loop; it does not use recursion, but relies on an interesting data structure, a set of intervals. It is not significantly longer or more difficult to understand than the traditional recursive version

sort (i, j: INTEGER)
……..require
……..……..i <= j
……..……..i >= a.lower
……..……..j <= a.upper
……..do
……..……if j > i then                    — Note that precondition of partition holds.
……..……..…..partition (i, j)         — Split into two slices s and t such that s <= t.
……..……..…..sort (i, pivot)          — Recursively sort first slice.
……..……..…..sort (pivot+1, j)      — Recursively sort second slice.
……..……end……..…..
……..end

Lampsort, in its author’s view, captures the true idea of Quicksort; the recursive version, and its parallelized variants, are only examples of possible implementations.

I wrote at the start that the focus of this article is Lampsort as an algorithm, not issues of methodology. Let me, however, give an idea of the underlying methodological debate. Lamport uses this example to emphasize the difference between algorithms and programs, and to criticize the undue attention being devoted to programming languages. He presents Lampsort in a notation which he considers to be at a higher level than programming languages, and it is for him an algorithm rather than a program. Programs will be specific implementations guided in particular by efficiency considerations. One can derive them from higher-level versions (algorithms) through refinement. A refinement process may in particular remove or restrict non-determinism, present in the above version of Lampsort through the query item (whose only official property is that it returns an element of the set).

The worldview underlying the Eiffel method is almost the reverse: treating the whole process of software development as a continuum; unifying the concepts behind activities such as requirements, specification, design, implementation, verification, maintenance and evolution; and working to resolve the remaining differences, rather than magnifying them. Anyone who has worked in both specification and programming knows how similar the issues are. Formal specification languages look remarkably like programming languages; to be usable for significant applications they must meet the same challenges: defining a coherent type system, supporting abstraction, providing good syntax (clear to human readers and parsable by tools), specifying the semantics, offering modular structures, allowing evolution while ensuring compatibility. The same kinds of ideas, such as an object-oriented structure, help on both sides. Eiffel as a language is the notation that attempts to support this seamless, continuous process, providing tools to express both abstract specifications and detailed implementations. One of the principal arguments for this approach is that it supports change and reuse. If everything could be fixed from the start, maybe it could be acceptable to switch notations between specification and implementation. But in practice specifications change and programs change, and a seamless process relying on a single notation makes it possible to go back and forth between levels of abstraction without having to perform repeated translations between levels. (This problem of change is, in my experience, the biggest obstacle to refinement-based approaches. I have never seen a convincing description of how one can accommodate specification changes in such a framework without repeating the whole process. Inheritance, by the way, addresses this matter much better.)

The example of Lampsort in Eiffel suggests that a good language, equipped with the right abstraction mechanisms, can be effective at describing not only final implementations but also abstract algorithms. It does not hurt, of course, that these abstract descriptions can also be executable, at the possible price of non-optimal performance. The transformation to an optimal version can happen entirely within the same method and language.

Quite apart from these discussions of software engineering methodology, Lamport’s elegant version of Quicksort deserves to be known widely.

References

[1] Lamport video here, segment starting at 0:32:34.
[2] Carlo Furia, Bertrand Meyer and Sergey Velder: Loop invariants: Analysis, Classification and Examples, in ACM Computing Surveys, September 2014, preliminary text here.

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New MOOC opens Tuesday

Our online course Computing: Art, Magic, Science, available from EdX, opens this Tuesday (tomorrow, 30 September) at 9 AM Zurich time (and at this time in your area).

An earlier article on this blog described the course, which integrates ten years of experience teaching introductory programming at ETH, and takes advantage of remote-compilation and remote-execution technology from our distributed development research.

You can find the course here.

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A gold medal

The French National Research Center (CNRS) has just awarded [1] its annual gold medal to Gérard Berry, a great recognition for an outstanding computer scientist. I first discovered Berry’s work through a brilliant 1976 article, Bottom-Up Computation of Recursive Programs [2], which explained recursion using methods of denotational semantics, and should really figure in collections of classic CS articles. One can hardly understand dynamic programming algorithms, for example, without realizing that they are bottom-up versions of recursive algorithms, through transformations described in the article. I relied extensively on its techniques for my own textbooks, from the advanced concepts of Introduction to the Theory of Programming Languages all the way down to the introductory presentation of recursion in Touch of Class.

Berry then went on to play a founding role in synchronous languages, explaining (in a paper whose reference I don’t have right now) that he went through a revelation of sorts after realizing that the automatic-control industry needed languages completely different from those computer scientists typically love. He created the Estérel language and shepherded it all the way to industrialization, serving as Chief Scientist of Estérel Technologies in the 2000 decade, before coming back to research.

He is also well known in France as a popularizer of informatics (computer science) through a number of successful books aiming at a large audience. He was appointed the first professor of informatics at the Collège de France, and in his inaugural lecture series presented a sweeping description of how information technology advances, based on powerful scientific concepts, permeate today’s world and raise ever new challenges. Hardly could the CNRS have chosen to distinguish a better representative of our discipline.

References

[1] CNRS announcement: here.

[2] Gérard Berry, Bottom-Up Computation of Recursive Programs, in RAIRO (Revue Française d’Automatique, Informatique, Recherche Opérationnelle, Informatique Théorique), tome 10, no R1 (1976), pp. 47-82, available here.

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Record enrollment

First week of semester at ETH. The number of incoming informatics (computer science) students has reached an all-time high: 345; see the (bad-quality) picture from day one of “Introduction to Programming”. (And no, the gender distribution has not changed.) So much for fears that MOOCs and such will displace universities; in fact we have not one but two MOOCs to support the course. Computer science is back in fashion!

Introduction to Programming, ETH Zurich, September 2014, first day of class

 

 

 

 

 

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Computing: the Art, the Magic, the Science

 

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.

 

 

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Reading Notes: Single-Entry, Single-Exit

 

It is remarkable that almost half a century after Dijkstra’s goto article, and however copiously and reverently it may be cited, today’s programs (other than in Eiffel) are still an orgy of gotos. There are not called gotos, being described as constructs that break out of a loop or exit a routine in multiple places, but they are gotos all the same. Multiple routine exits are particularly bad since they are in effect interprocedural gotos.

Ian Joyner has just released a simple and cogent summary of why routines should always have one entry and one exit.

References

[1] Ian Joyner: Single-entry, single-exit (SESE) heuristic, available here.

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Accurately Analyzing Agility

  
Book announcement:

Agile! The Good, the Hype and the Ugly
Bertrand Meyer
Springer, 2014 (just appeared)
Book page: here.
Amazon page: here.
Publisher’s page: here

A few years ago I became fascinated with agile methods: with the unique insights they include; with the obvious exaggerations and plainly wrong advice they also promote; and perhaps most of all with the constant intermingling of these two extremes.

I decided to play the game seriously: I read a good part of the agile literature, including all the important books; I sang the song, became a proud certified Scrum Master; I applied many agile techniques in my own work.

The book mentioned above is the result of that study and experience. It is both a tutorial and a critique.

The tutorial component was, I felt, badly needed. Most of the agile presentations I have seen are partisan texts, exhorting you to genuflect and adopt some agile method as the secret to a better life. Such preaching has a role but professionals know there is no magic in software development.  Agile! describes the key agile ideas objectively, concretely, and as clearly as I could present them. It does not introduce them in a vacuum, like the many agile books that pretend software engineering did not exist before (except for a repulsive idea, the dreaded “waterfall”). Instead, it relates them to many other concepts and results of software engineering, to which they bring their own additions and improvements.

Unfortunately, not all the additions are improvements. Up to now, the field has largely been left (with the exception of Boehm’s and Turner’s 2005 “Guide for the Perplexed“) to propaganda pieces and adoring endorsements. I felt that software developers would benefit more from a reasoned critical analysis. All the more so that agile methods are a remarkable mix of the best and the worst; the book carefully weeds out — in the terminology of the title — the ugly from the hype and the truly good.

Software developers and managers need to know about the “ugly”: awful agile advice that is guaranteed to harm your project. The “hype” covers ideas that have been widely advertised as shining agile contributions but have little relevance to the core goals of software development. The reason it was so critical to identify agile ideas belonging to these two categories is that they detract from the “good”, some of it remarkably good. I would not have devoted a good part of the last five years to studying agile methods if I did not feel they included major contributions to software engineering. I also found that some of these contributions do not get, in the agile literature itself, the explanations and exposure they deserve; I made sure they got their due in the book. An example is the “closed-window rule”, a simple but truly brilliant idea, of immediate benefit to any project.

Software methodology is a difficult topic, on which we still have a lot to learn. I expect some healthy discussions, but I hope readers will appreciate the opportunity to discuss agile ideas in depth for the greater benefit of quality software development.

I also made a point of writing a book that (unlike my last two) is short: 190 pages, including preface, index and everything else.

The table of contents follows; more details and sample chapters can be found on the book page listed above.

Preface
1 OVERVIEW
     1.1 VALUES
     1.2 PRINCIPLES
          Organizational principles
          Technical principles
     1.3 ROLES
     1.4 PRACTICES
          Organizational practices
          Technical practices
     1.5 ARTIFACTS
          Virtual artifacts
          Material artifacts
     1.6 A FIRST ASSESSMENT
          Not new and not good
          New and not good
          Not new but good
          New and good!

2 DECONSTRUCTING AGILE TEXTS
     2.1 THE PLIGHT OF THE TRAVELING SEMINARIST
          Proof by anecdote
          When writing beats speaking
          Discovering the gems
          Agile texts: reader beware!
     2.2 THE TOP SEVEN RHETORICAL TRAPS
          Proof by anecdote
          Slander by association
          Intimidation
          Catastrophism
          All-or-nothing
          Cover-your-behind
          Unverifiable claims
          Postscript: you have been ill-served by the software industry!

&3 THE ENEMY: BIG UPFRONT ANYTHING
     3.1 PREDICTIVE IS NOT WATERFALL
     3.2 REQUIREMENTS ENGINEERING
          Requirements engineering techniques
          Agile criticism of upfront requirements
          The waste criticism
          The change criticism
          The domain and the machine
     3.3 ARCHITECTURE AND DESIGN
          Is design separate from implementation?
          Agile methods and design
     3.4 LIFECYCLE MODELS
     3.5 RATIONAL UNIFIED PROCESS
     3.6 MATURITY MODELS
          CMMI in plain English
          The Personal Software Process
          CMMI/PSP and agile methods
          An agile maturity scale

4 AGILE PRINCIPLES
     4.1 WHAT IS A PRINCIPLE?
     4.2 THE OFFICIAL PRINCIPLES
     4.3 A USABLE LIST
     4.4 ORGANIZATIONAL PRINCIPLES
          Put the customer at the center
          Let the team self-organize
          Maintain a sustainable pace
          Develop minimal software
          Accept change
     4.5 TECHNICAL PRINCIPLES
          Develop iteratively
          Treat tests as a key resource
          Do not start any new development until all tests pass
          Test first
          Express requirements through scenarios

5 AGILE ROLES
     5.1 MANAGER
     5.2 PRODUCT OWNER
     5.3 TEAM
          Self-organizing
          Cross-functional
     5.4 MEMBERS AND OBSERVERS
     5.5 CUSTOMER
     5.6 COACH, SCRUM MASTER
     5.7 SEPARATING ROLES

6 AGILE PRACTICES: MANAGERIAL
     6.1 SPRINT
          Sprint basics
          The closed-window rule
          Sprint: an assessment
     6.2 DAILY MEETING
     6.3 PLANNING GAME
     6.4 PLANNING POKER
     6.5 ONSITE CUSTOMER
     6.6 OPEN SPACE
     6.7 PROCESS MINIATURE
     6.8 ITERATION PLANNING
     6.9 REVIEW MEETING
     6.10 RETROSPECTIVE
     6.11 SCRUM OF SCRUMS
     6.12 COLLECTIVE CODE OWNERSHIP
          The code ownership debate
          Collective ownership and cross-functionality

7 AGILE PRACTICES: TECHNICAL
     7.1 DAILY BUILD AND CONTINUOUS INTEGRATION
     7.2 PAIR PROGRAMMING
          Pair programming concepts
          Pair programming versus mentoring
          Mob programming
          Pair programming: an assessment
     7.3 CODING STANDARDS
     7.4 REFACTORING
          The refactoring concept
          Benefits and limits of refactoring
          Incidental and essential changes
          Combining a priori and a posteriori approaches
     7.5 TEST-FIRST AND TEST-DRIVEN DEVELOPMENT
          The TDD method of software development
          An assessment of TFD and TDD

8 AGILE ARTIFACTS
     8.1 CODE
     8.2 TESTS
     8.3 USER STORIES
     8.4 STORY POINTS
     8.5 VELOCITY
     8.6 DEFINITION OF DONE
     8.7 WORKING SPACE
     8.8 PRODUCT BACKLOG, ITERATION BACKLOG
     8.9 STORY CARD, TASK CARD
     8.10 TASK AND STORY BOARDS
     8.11 BURNDOWN AND BURNUP CHARTS
     8.12 IMPEDIMENT
     8.13 WASTE, TECHNICAL DEBT, DEPENDENCY, DEPENDENCY CHARTS

9 AGILE METHODS
     9.1 METHODS AND METHODOLOGY
          Terminology
          The fox and the hedgehog
     9.2 LEAN SOFTWARE AND KANBAN
          Lean Software’s Big Idea
          Lean Software’s principles
          Lean Software: an assessment
          Kanban
     9.3 EXTREME PROGRAMMING
          XP’s Big Idea
          XP: the unadulterated source
          Key XP techniques
          Extreme Programming: an assessment
     9.4 SCRUM
          Scrum’s Big Idea
          Key Scrum practices
          Scrum: an assessment
     9.5 CRYSTAL
          Crystal’s Big Idea
          Crystal principles
          Crystal: an assessment

10 DEALING WITH AGILE TEAMS
     10.1 GRAVITY STILL HOLDS
     10.2 THE EITHER-WHAT-OR-WHEN FALLACY

11 THE UGLY, THE HYPE AND THE GOOD: AN ASSESSMENT OF THE AGILE APPROACH
     11.1 THE BAD AND THE UGLY
          Deprecation of upfront tasks
          User stories as a basis for requirements
          Feature-based development and ignorance of dependencies
          Rejection of dependency tracking tools
          Rejection of traditional manager tasks
          Rejection of upfront generalization
          Embedded customer
          Coach as a separate role
          Test-driven development
          Deprecation of documents
     11.2 THE HYPED
     11.3 THE GOOD
     11.4 THE BRILLIANT
Bibliography
Index

 

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Crossing the Is and doting on the Ts

 

Last week at the the CSEE&T conference in Klagenfurt (the conference page is here, I gave a keynote), a panel discussed how universities should prepare students for software engineering. Barry Boehm, one of the panelists, stated the following principle, which afterwards he said he had learned from Simon Ramo, co-founder of TRW. In hiring people, he stated, it is better to avoid candidates with an I-shaped profile: narrowly specialized in one topic that they have explored to exhaustion. Better look for a T: someone who has mastered an area in depth and then branched out to learn about many others.

I started playing with the variants. One should avoid the hyphens, or em-dashes, ““: people with a smattering of everything but no detailed knowledge of anything. Boehm said that this is the reason he always argued against establishing such undergraduate majors as systems engineering. A variant of the hyphen is the overline ““: graduates who supposedly are so smart that they can learn anything, but whose actual knowledge is limited to abstract notions.

Along with the T we should consider the “bottom” symbol of denotational semantics: . It corresponds to people who have a broad educational base, for example in mathematics, and have deepened it by focusing on a particular topic. The T and can be combined into an H turned on its side, H on the side: acquiring a solid foundation, specializing, then using that experience to become familiar with new areas.

Extending the permutation group, I am not sure what a “+” profile would be, but in a discussion last night Rustan Leino and Peter Müller suggested the “O”, ability to to circle around topics, and the umlaut, knowing a thing or two; in fact, exactly two.

 

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Learning to program, online

Introduction to Programming MOOCThe ETH introductory programming course, which I have taught since 2003 and used as the basis for the Springer Touch of Class textbook, is now available as a MOOC: an online course, open to anyone interested [1]. The project was started and led by Marco Piccioni.

The MOOC was released in September; although it was “open” (the other “O”) from the start, we have not publicized it widely until now, since we used it first for the benefit of students taking the course at ETH, and took advantage of this experience to polish it. If you follow the acronym buzz you may say it was first a “SPOC” (Small Personal Online Course”). The experience with our students has been extremely encouraging: they took it as a supplement to the lectures and widely praised its value.We hope that many others will find it useful as well.

MOOCs are hot but they have attracted as much criticism as hype. We have seen the objections: low completion rates, lack of direct human contact, threats to traditional higher education. Two things are clear, though: MOOCs are more than a passing fad; and they have their own pedagogical advantages.

MOOCs are here to stay; one ignores them at one’s own peril. For courses on a popular topic, I believe that in a few years almost everyone will be teaching from a MOOC. Not in the sense of telling students “just follow this course on the Web and come back for the exam“, but as a basis for individual institutions’ courses. For example the students might be told to take the lessons online, then come to in-person lectures (“Flip The Classroom“) or discussion sessions. For any given topic, such as introduction to programming, only a handful of MOOCs will emerge in this role. We would like the ETH course to be one of them.

The question is not just to replace courses and textbooks with an electronic version. MOOCs enrich the learning experience. Introduction to Programming is a particularly fertile application area for taking advantage of technology: the presentation of programming methods and techniques becomes even more effective if students can immediately try out the ideas, compile the result, run it, and see the results on predefined tests. Our course offers many such interactive exercises, thanks to the E4Mooc (Eiffel for MOOCs) framework developed by Christian Estler [2]. This feature has proved to be a key attraction of the course for ETH students. Here for example is an exercise asking you to write a function that determines whether a string is a palindrome (reads identically in both directions):

The program area is pre-filled with a class skeleton where all that remains for you to enter is the algorithm for the relevant function. Then you can click “Compile” and, if there are no compilation errors, “Run” to test your candidate code against a set of predefined test values. One of the benefits for users taking the course is that there is no software to install: everything runs in the cloud, accessible from the browser. Here we see the MOOC not just as a technique for presenting standard material but as an innovative learning tool, opening up pedagogical techniques that were not previously possible.

Besides E4Mooc, our course relies on the Moodle learning platform. Our experience with Moodle has been pleasant; we noticed, for example, that students really liked the Moodle feature enabling them to gain virtual “badges” for good answers, to the point of repeating exercises until they got the badges. For instructors preparing the course, building a MOOC is a huge amount of work (that was not a surprise, people had told us); but it is worth the effort.

We noted that attendance at the lectures increased as compared to previous years. The matter is a natural concern: other than the cold November mornings in Zurich (one of the lectures is at 8 AM) there are many reasons not to show up in class:  the textbook covers much of the material; all the slides are online; so are the slides for exercise sessions (tutorials) and texts of exercises and some earlier exams; lectures were video-recorded in previous years, and students can access the old recordings. Our feeling is that the MOOC makes the course more exciting and gives students want to come to class and hear more.

The MOOC course is not tied to a particular period; you can take it whenever you like. (The current practice of offering MOOCs at fixed times is disappointing: what is the point of putting a course online if participants are forced to fit to a fixed schedule?)

Marco Piccioni and I are now off to our second MOOC, which will be a generalization of the first, covering the basics not just of programming but of computer science and IT overall, and will be available on one of the major MOOC platforms. We will continue to develop the existing MOOC, which directly supports our in-person course, and which we hope will be of use to many other people.

Take the MOOC, or tell a beginner near you to take it, and tell us what you think.

References

[1] Bertrand Meyer, Marco Piccioni and other members of the ETH Chair of Software Engineering: ETH Introduction to Programming MOOC, available here.

[2] H-Christian Estler, E4Mooc demo, available on YouTube: here.

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LASER 2014 (Elba, September)

2014 marks the 10-th anniversary (11th edition) of the LASER summer school. The school will be held September 7-14, 2014, and the detailed information is here.

LASER (the name means Laboratory for Applied Software Engineering Research) is dedicated to practical software engineering. The roster of speakers since we started is a who’s who of innovators in the field. Some of the flavor of the school can gathered from the three proceedings volumes published in Springer LNCS (more on the way) or simply by browsing the pages of the schools from previous years.

Usually we have a theme, but to mark this anniversary we decided to go for speakers first; we do have a title, “Leading-Edge Software Engineering”, but broad enough to encompass a wide variety of a broad range of topics presented by star speakers: Harald Gall, Daniel Jackson, Michael Jackson, Erik Meijer (appearing at LASER for the third time!), Gail Murphy and Moshe Vardi. With such a cast you can expect to learn something important regardless of your own primary specialty.

LASER is unique in its setting: a 5-star hotel in the island paradise of Elba, with outstanding food and countless opportunities for exploring the marvelous land, the beaches, the sea, the geology (since antiquity Elba has been famous for its stones and minerals) and the history, from the Romans to Napoleon, who in the 9 months of his reign changed the island forever. The school is serious stuff (8:30 to 13 and 17 to 20 every day), but with enough time to enjoy the surroundings.

Registration is open now.

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Niklaus Wirth birthday symposium, 20 February, Zurich

In honor of Niklaus Wirth’s 80-th birthday we are organizing a symposium at ETH on February 20, 2014. This is a full-day event with invited talks by:

  • Vint Cerf
  • Hans Eberlé
  • Michael Franz
  • me
  • Carroll Morgan
  • Martin Odersky
  • Clemens Szyperski
  • Niklaus Wirth himself

From the symposium’s web page:

Niklaus Wirth was a Professor of Computer Science at ETH Zürich, Switzerland, from 1968 to 1999. His principal areas of contribution were programming languages and methodology, software engineering, and design of personal workstations. He designed the programming languages Algol W, Pascal, Modula-2, and Oberon, was involved in the methodologies of structured programming and stepwise refinement, and designed and built the workstations Lilith and Ceres. He published several text books for courses on programming, algorithms and data structures, and logical design of digital circuits. He has received various prizes and honorary doctorates, including the Turing Award, the IEEE Computer Pioneer, and the Award for outstanding contributions to Computer Science Education.

Participation is free (including breaks, lunch and the concluding “Apéro”) but space is strictly limited and we expect to run out of seats quickly. So if you are interested (but only if you are certain to attend) please register right away.

Symposium page and access to registration form: here.

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Informatics education in Europe: Just the facts

 

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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Hungarian rotation

The 2013 Informatics Europe “Best Practices in Education” award was devoted, this year, to initiatives for teaching informatics in schools [1].  It was given out last week at the European Computer Science Summit in Amsterdam [2]. Two teams shared it, one from Poland and the other from Romania. Both teams showed excellent projects, but the second was beyond anything I expected.

The project comes from the Hungarian-speaking Sapientia University in Transylvania and is devoted to teaching algorithms visually and “at the same time enhancing intercultural communication” in the region. It illustrates the classical sorting algorithms through folk dances. Quicksort is Hungarian, selection sort is gypsy, merge sort is “Transylvanian-saxon”. I think my favorite is Shell sort [3]. For more, see their YouTube channel [4].

Now  if only they could act the loop invariants [5].

References

[1] 2013 Best Practices in Education award, see here.

[2] 2013 European Computer Science Summit, here.

[3] “Shell sort with Hungarian (Székely) folk dance”, see here.

[4] YouTube Algorythmics channel, here.

[5] Carlo Furia, Bertrand Meyer and Sergey Velder: Loop invariants: Analysis, Classification and Examples, in ACM Computing Surveys, Septembre 2014, to appear, available here.

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Smaller, better textbook

A new version of my Touch of Class [1] programming textbook is available. It is not quite a new edition but more than just a new printing. All the typos that had been reported as of a few months ago have been corrected.

The format is also significantly smaller. This change is more than a trifle. When а  reader told me for the first time “really nice book, pity it is so heavy!”, I commiserated and did not pay much attention. After twenty people said that, and many more after them, including professors looking for textbooks for their introductory programming classes, I realized it was a big deal. The reason the book was big and heavy was not so much the size of the contents (876 is not small, but not outrageous for a textbook introducing all the fundamental concepts of programming). Rather, it is a technical matter: the text is printed in color, and Springer really wanted to do a good job, choosing thick enough paper that the colors would not seep though. In addition I chose a big font to make the text readable, resulting in a large format. In fact I overdid it; the font is bigger than necessary, even for readers who do not all have the good near-reading sight of a typical 19-year-old student.

We kept the color and the good paper,  but reduced the font size and hence the length and width. The result is still very readable, and much more portable. I am happy to make my contribution to reducing energy consumption (at ETH alone, think of the burden on Switzerland’s global energy bid of 200+ students carrying the book — as I hope they do — every morning on the buses, trains and trams crisscrossing the city!).

Springer also provides electronic access.

Touch of Class is the textbook developed on the basis of the Introduction to Programming course [2], which I have taught at ETH Zurich for the last ten years. It provides a broad overview of programming, starting at an elementary level (zeros and ones!) and encompassing topics not usually covered in introductory courses, even a short introduction to lambda calculus. You can get an idea of the style of coverage of such topics by looking up the sample chapter on recursion at touch.ethz.ch. Examples of other topics covered include a general introduction to software engineering and software tools. The presentation uses full-fledged object-oriented concepts (including inheritance, polymorphism, genericity) right from the start, and Design by Contract throughout. Based on the “inverted curriculum” ideas on which I published a number of articles, it presents students with a library of reusable components, the Traffic library for graphical modeling of traffic in a city, and builds on this infrastructure both to teach students abstraction (reusing code through interfaces including contracts) and to provide them models of high-quality code for imitation and inspiration.

For more details see the article on this blog that introduced the book when it was first published [3].

References

[1] Bertrand Meyer, Touch of Class: An Introduction to Programming Well Using Objects and Contracts, Springer Verlag, 2nd printing, 2013. The Amazon page is here. See the book’s own page (with slides and other teaching materials, sample chapter etc.) here. (Also available in Russian, see here.)

[2] Einführung in die Programmierung (Introduction to Programming) course, English course page here.

[3] Touch of Class published, article on this blog, 11 August 2009, see [1] here.

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The invariants of key algorithms (new paper)

 

I have mentioned this paper before but as a draft. It has now been accepted by ACM’s Computing Surveys and is scheduled to appear in September 2014; the current text, revised from the previous version, is available [1].

Here is the abstract:

Software verification has emerged as a key concern for ensuring the continued progress of information technology. Full verification generally requires, as a crucial step, equipping each loop with a “loop invariant”. Beyond their role in verification, loop invariants help program understanding by providing fundamental insights into the nature of algorithms. In practice, finding sound and useful invariants remains a challenge. Fortunately, many invariants seem intuitively to exhibit a common flavor. Understanding these fundamental invariant patterns could therefore provide help for understanding and verifying a large variety of programs.

We performed a systematic identification, validation, and classification of loop invariants over a range of fundamental algorithms from diverse areas of computer science. This article analyzes the patterns, as uncovered in this study,governing how invariants are derived from postconditions;it proposes a taxonomy of invariants according to these patterns, and presents its application to the algorithms reviewed. The discussion also shows the need for high-level specifications based on “domain theory”. It describes how the invariants and the corresponding algorithms have been mechanically verified using an automated program prover; the proof source files are available. The contributions also include suggestions for invariant inference and for model-based specification.

Reference

[1] Carlo Furia, Bertrand Meyer and Sergey Velder: Loop invariants: Analysis, Classification and Examples, in ACM Computing Surveys, to appear in September 2014, preliminary text available here.

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Informatics: catch them early

 

Recycled[I occasionally post on the Communications of the ACM blog. It seems that there is little overlap between readers of that blog and the present one, so — much as I know, from software engineering, about the drawbacks of duplication — I will continue to repost articles here when relevant.]

Some call it computer science, others informatics, but they face the same question: when do we start teaching the subject? In many countries where high schools began to introduce it in the seventies, they actually retreated since then; sure, students are shown how to use word processors and spreadsheets, but that’s not the point.

Should we teach computer science in secondary (and primary) school? In a debate at SIGCSE a few years ago, Bruce Weide said in strong words that we should not: better give students the strong grounding in mathematics and especially logic that they will need to become good at programming and CS in general. I found the argument convincing: I teach first-semester introductory programming to 200 entering CS students every year, and since many have programming experience, of highly diverse nature but usually without much of a conceptual basis, I find myself unteaching a lot. In a simple world, high-school teachers would teach students to reason, and we would teach them to program. The world, however, is not simple. The arguments for introducing informatics earlier are piling up:

  • What about students who do not enter CS programs?
  • Many students will do some programming anyway. We might just as well teach them to do it properly, rather than let them develop bad practices and try to correct them at the university level.
  • Informatics is not just a technique but an original scientific discipline, with its own insights and paradigms (see [2] and, if I may include a self-citation, [3]). Its intellectual value is significant for all educated citizens, not just computer scientists.
  • Countries that want to be ahead of the race rather than just consumers of IT products need their population to understand the basic concepts, just as they want everyone, and not just future mathematicians, to master the basics of arithmetics, algebra and geometry.

These and other observations led Informatics Europe and ACM Europe, two years ago, to undertake the writing of a joint report, which has now appeared [1]. The report is concise and makes strong points, emphasizing in particular the need to distinguish education in informatics from a mere training in digital literacy (the mastery of basic IT tools, the Web etc.). The distinction is often lost on the general public and decision-makers (and we will surely have to emphasize it again and again).

The report proposes general principles for both kinds of programs, emphasizing in particular:

  • For digital literacy, the need to teach not just how-tos but also safe, ethical and effective use of IT resources and tools.
  • For informatics, the role of this discipline as a cross-specialty subject, like mathematics.

The last point is particularly important since we should make it clear that we are not just pushing (out of self-interest, as members of any discipline could) for schools to give our specialty a share, but that informatics is a key educational, scientific and economic resource for the citizens of any modern country.

The report is written from a European perspective, but the analysis and conclusions will, I think, be useful in any country.

It does not include any detailed curriculum recommendation, first because of the wide variety of educational contexts, but also because that next task is really work for another committee, which ACM Europe and Informatics Europe are in the process of setting up. The report also does not offer a magic solution to the key issue of bootstrapping the process — by finding teachers to make the courses possible, and courses to justify training the teachers — but points to successful experiences in various countries that show a way to break the deadlock.

The introduction of informatics as a full-fledged discipline in the K-12 curriculum is clearly where the winds of history are blowing. Just as the report was being finalized, the UK announced that it was making CS one of the choices of required scientific topics would become a topic in the secondary school exam on a par with traditional sciences. The French Academy of Sciences recently published its own report on the topic, and many other countries have similar recommendations in progress. The ACM/IE report is a major milestone which should provide a common basis for all these ongoing efforts.

References

[1] Informatics education: Europe Cannot Afford to Miss the Boat,  Report of the joint Informatics Europe & ACM Europe Working Group on Informatics Education,  April 2013,  available here.

[2] Jeannette Wing: Computational Thinking, in Communications of the ACM, vol. 49, no. 3, March 2006, pages 33-35, available here.

[3] Bertrand Meyer: Software Engineering in the Academy, in Computer (IEEE), vol. 34, no. 5, May 2001, pages 28-35, available here.

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New course partners sought: a DOSE of software engineering education

 

Since 2007 we have conducted, as part of a course at ETH, the DOSE project, Distributed and Outsourced Software Engineering, developed by cooperating student teams from a dozen universities around the world. We are finalizing the plans for the next edition, October to December 2013, and will be happy to welcome a few more universities.

The project consists of building a significant software system collaboratively, using techniques of distributed software development. Each university contributes a number of “teams”, typically of two or three students each; then “groups”, each made up of three teams from different universities, produce a version of the project.

The project’s theme has varied from year to year, often involving games. We make sure that the development naturally divides into three subsystems or “clusters”, so that each group can quickly distribute the work among its teams. An example of division into clusters, for a game project, is: game logic; database and player management; user interface. The page that describes the setup in more detail [1] has links enabling you to see the results of some of the best systems developed by students in recent years.

The project is a challenge. Students are in different time zones, have various backgrounds (although there are minimum common requirements [1]), different mother tongues (English is the working language of the project). Distributed development is always hard, and is harder in the time-constrained context of a university course. (In industry, while we do not like that a project’s schedule slips, we can often survive if it does; in a university, when the semester ends, we have to give students a grade and they go away!) It is typical, after the initial elation of meeting new student colleagues from exotic places has subsided and the reality of interaction sets in, that some groups will after a month, just before the first or second deadline, start to panic — then take matters into their own hands and produce an impressive result. Students invariably tell us that they learn a lot through the course; it is a great opportunity to practice the principles of modern software engineering and to get prepared for the realities of today’s developments in industry, which are in general distributed.

For instructors interested in software engineering research, the project is also a great way to study issues of distributed development in  a controlled setting; the already long list of publications arising from studies performed in earlier iterations [3-9] suggests the wealth of available possibilities.

Although the 2013 project already has about as many participating universities as in previous years, we are always happy to consider new partners. In particular it would be great to include some from North America. Please read the requirements on participating universities given in [1]; managing such a complex process is a challenge in itself (as one can easily guess) and all teaching teams must share goals and commitment.

References

[1] General description of DOSE, available here.

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

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

[4] Martin Nordio, Roman Mitin, Bertrand Meyer, Carlo Ghezzi, Elisabetta Di Nitto and Giordano Tamburelli: The Role of Contracts in Distributed Development, in Proceedings of Software Engineering Advances For Offshore and Outsourced Development, Lecture Notes in Business Information Processing 35, Springer-Verlag, 2009, available here.

[5] Martin Nordio, Roman Mitin and Bertrand Meyer: Advanced Hands-on Training for Distributed and Outsourced Software Engineering, in Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering – Volume 1, ACM. 2010 available here.

[6] Martin Nordio, Carlo Ghezzi, Bertrand Meyer, Elisabetta Di Nitto, Giordano Tamburrelli, Julian Tschannen, Nazareno Aguirre and Vidya Kulkarni: Teaching Software Engineering using Globally Distributed Projects: the DOSE course, in Collaborative Teaching of Globally Distributed Software Development – Community Building Workshop (CTGDSD — an ICSE workshop), ACM, 2011, available here.

[7] Martin Nordio, H.-Christian Estler, Bertrand Meyer, Julian Tschannen, Carlo Ghezzi, and Elisabetta Di Nitto: How do Distribution and Time Zones affect Software Development? A Case Study on Communication, in Proceedings of the 6th International Conference on Global Software Engineering (ICGSE), IEEE, pages 176–184, 2011, available here.

[8] H.-Christian Estler, Martin Nordio, Carlo A. Furia, and Bertrand Meyer: Distributed Collaborative Debugging, to appear in Proceedings of 7th International Conference on Global Software Engineering (ICGSE), 2013.

[9] H.-Christian Estler, Martin Nordio, Carlo A. Furia, and Bertrand Meyer: Unifying Configuration Management with Awareness Systems and Merge Conflict Detection, to appear in Proceedings of the 22nd Australasian Software Engineering Conference (ASWEC), 2013.

 

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The education minister who wants fewer students

Picture yourself an incoming education minister in one of the EU countries — Germany, France, UK — who declares that he would like fewer students to graduate and go to university. Imagine the clamor. Even in the US  — where the secretary of education does not in fact have much sway over high schools, managed locally, or universities, controlled by the states or by private organizations — outrage would erupt. Assume for good measure that he criticizes immigrants for pushing their children to educate themselves. Pretty unthinkable.

Johann Schneider-Ammann will be education minister of Switzerland starting in 2013. (The seeming innocuousness of this factual statement belies the uniqueness of the situation: rather than ministries in the usual sense, Switzerland has federal departments, and their management rotates among the seven Federal Counselors — as does, yearly, the presidency of the Confederation. But that topic is for another day.) In a recent interview [1], Schneider-Ammann states that it would be a grave danger to allow any further growth of the percentage of students graduating with the high-school degree, the “Maturity” or  in common parlance Matura (equivalent to the German Abitur and the French Baccalauréat). What is this scary threshold? The graduation rate (France: 84.5%) has in Switzerland grown in the past years from 12% to a whopping 20%. This is where the minister wants to raise a red flag.

Not stopping there, he bitterly complains that immigrant families “want their children to get a Matura at any price”. These immigrant’s conceit has no bound! Can you fathom the insolence: they want to educate their kids!

Were such declarations to come from Mr. Schneider-Ammann’s French counterpart, the streets of Paris would fill up with pitchfork-brandishing youngsters. In the US, no one would even understand the part about immigrants: walk the halls of Berkeley or Stanford and it’s Asians everywhere, since childhood pushed to excellence by their “Chinese mothers” [2] or equivalent.

What is going on? Has Switzerland put in charge of its education the equivalent of (in the US) the would-be Republican candidate Rick Santorum, who infamously proclaimed that “President Obama wants everybody in America to go to college. What a snob!”.

Well, to a point, yes. But Schneider-Ammann, an ETH graduate in electrical engineering, is not an obscurantist and not driven by religious extremism. What he is talking about is the uniqueness of the Swiss educational system, which includes a separation of students at the age of 12 between those who will pursue the Matura, leading to open admission to almost any university program [3], and those channeled to technical tracks with reduced teaching hours and extensive on-the-job training. That system explains the 20% figure: it is not that the other 80% are left to rot; most of them receive a job-oriented qualification and a technical degree. Anyone who has tried to use the services of a plumber in the United States and in Switzerland understands the effect of this system on the quality of professional work (and its price).

Schneider-Ammann (along with, in my experience, most education professionals in Switzerland) has no qualms about defending that system. He says:

Every society is a kind of pyramid with, at the top, the most intellectual people and those with the most predisposition to education, and a wide base made of people with essentially manual skills. We have to include these in our education system as well. This is the only way to remain competitive and innovative and keep everyone, to all the extent possible, in the employment process.

In many circles such an unabated view would be howled down as elitist and paternalistic. The Swiss, however, have little interest in the kind of abstract arguments that are popular among French and German intellectuals. They are pragmatic and look at the results. Schneider-Ammann is not shy in pointing the fingers at other countries:

The more high-shool graduates a country has, the higher its unemployment rate. The relationship is obvious when one looks at the statistics. Highfalutin education plays its part in deindustrialisation. We can see it in Great Britain or France.

The views on immigrants are in the same spirit. Think not of mathematically brilliant Asian students forcefully entering computer science at MIT, but of children of families — for example, as Schneider-Ammann  helpfully explains lest anyone fear ambiguity, “from Germany or France”— which “come to Switzerland and from the experience of their country of origin know hardly anything else than the academic road to education”. Ah, these German mothers who know “hardly anything else” than universities! These French fathers who do not wake up at night worrying whether their daughters will make it to tram driver!

These arguments will, one guesses, make for interesting conversations when he does become minister and gets to meet his foreign colleagues, but they are hard to ignore. What do the statistics actually say?

From OECD documents, e.g. [4], I do not completely understand the British picture (not much of a comment since there are few things I understand about Britain). In  France, where reaching a 80% rate of success at the Baccalauréat was a decades-old political goal and a cause for national celebration when reached a few years ago, the unemployment  is currently 9.5% and shows no sign of abating (that is an optimistic way of putting it). Significantly, high unemployment  is not a fluke resulting from the current  economic crisis but a persistent problem going back at least to the eighties and clearly resulting from structural causes. In Germany, for all its economic strength, the rate is hardly better, having oscillated between 9% and 11% between 2002 and 2007 and remaining around 7% in 2012.

In Switzerland: 3% today, and never above 4% since 2001. (In early 2001 it was around 1.6%!) As to the educational level of the population, the OECD notes [3] that  Switzerland is a top-performing OECD country in reading literacy, maths and sciences with the average student scoring 517.

Correlation is not causation; politicians simplify complex matters, and one can think of a few counter-examples to Mr. Schneider-Ammann’s reasoning (I would like to get a better idea of the Finnish picture, and Korea also seems an interesting case). Still, that reasoning has to be taken seriously. Anyone familiar with the French situation, for example, can only wonder what good it is to give everyone the Bac and access to overcrowded university tracks of sociology, ethnography and psychology. How many ethnographers does a country need? Since the world is selective, selection occurs anyway, if after the  Bac, and most notably in controlling access to the noblest part of the system — the top of Schneider-Ammann’s pyramid: the Grandes Écoles, which are unabashedly elitist. Families in the know understand that the competitive examinations to Polytechnique and the like, not the Bac, are the exams that count. This part of the system, the royal track, works very well; I had the immense privilege of benefiting from it and can testify to its efficiency. It is at least as exclusive as the Swiss Matura+University track. The problem is the rest of the system; those students who do not make it to the top are somehow herded to the Bac and the first years of ordinary universities without the appropriate support and infrastructure.

Thereby lies the difference: the Swiss have no patience for grand speeches about high education, the implicit promise that everyone can become Jean-Paul Sartre or Simone de Beauvoir, and the harsh accompanying reality of a system that hides cruel disparities behind the appearance of universal access. Instead, they bluntly sort out at a tender age [5] the few intellectuals from the many practically-oriented students. The big difference with some other countries is that the latter category is neither duped nor dumped: neither duped into believing they can have an high-flying university education, nor dumped to mend for themselves. The technical and apprenticeship programs are are seriously organized, well-funded, and intended to lead to stable, respected professions.

So far the system has worked incredibly well; the durably low unemployment rate, in sharp contrast with neighboring countries, is only one sign of the country’s success. I do not know how much of the correlation is causation, and how much the Swiss experience is transposable to other countries.

As an intellectual, and someone who gained so much from education in peerless institutions, I do not feel in a good position to decree that others should just learn a trade.  But I find the argument fascinating. The conventional wisdom today is that countries must educate, educate, educate. Usually this is understood as pushing ever more students towards academic tracks. There are a few dissenting voices; Paul Krugman, for example, has regularly warned that automation today threatens low-end intellectual jobs (he comes back to that theme in today’s New York Times [5]). I do not know the answer; but the questions are worth asking, without fear of breaking taboos.

References and notes

[1] «Ich hätte lieber etwas weniger, dafür bessere Maturanden» (I’d rather have somewhat fewer and hence better high-school graduates), interview of Johann Schneider-Ammann (in German),  by René Donzé and Sarah Nowotny, Neue Zürcher Zeitung, 28 October 2012, available here.

[2] Amy Chua, Battle Hymn of the Tiger Mother, Penguin Press, 2011, see summary here.

[3] Law and medicine have a numerus clausus. Students graduating with a Matura can otherwise enter the university and program of their choice.

[4] OECD Better Life index, here. Note that the OECD reports give Switzerland a high-school graduation rate of 90%, at the very top of countries surveyed, meaning that the rate does not distinguish between the various kinds of high-school certificates. High-school graduation rates as discussed in the present article refer to the standard academic tracks, which for Switzerland means the Matura not including professional tracks.

[5] Migration paths exist, for hard-working late bloomers who want to transition from the lower-tier system to the universities.

[6] Paul Krugman: Robots and Robber Barons, New York Times, 9 December 2012, available here.

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