### Archive

Archive for April, 2018

## A Python Data Science hackathon

April 23rd, 2018 (17 hours ago) No comments

I was at the Man AHL Hackathon this weekend. The theme was improving the Python Data Science ecosystem. Around 15, or so, project titles had been distributed around the tables in the Man AHL cafeteria and the lead person for each project gave a brief presentation. Stable laws in SciPy sounded interesting to me and their room location included comfy seating (avoiding a numb bum is an under appreciated aspect of choosing a hackathon team and wooden bench seating is not numbing after a while).

Team Stable laws consisted of Andrea, Rishabh, Toby and yours truly. Our aim was to implement the Stable distribution as a Python module, to be included in the next release of SciPy (the availability had been announced a while back and there has been one attempt at an implementation {which seems to contain a few mistakes}).

We were well-fed and watered by Man AHL, including fancy cream buns and late night sushi.

A probability distribution is stable if the result of linear combinations of the distribution has the same distribution; the Gaussian, or Normal, distribution is the most well-known stable distribution and the central limit theorem leads many to think, that is that.

Two other, named, stable distributions are the Cauchy distribution and most interestingly (from my perspective this weekend) the Lévy distribution. Both distributions have very fat tails; the mean and variance of the Cauchy distribution is undefined (i.e., the values jump around as the sample size increases, never converging to a fixed value), while they are both infinite for the Lévy distribution.

Analytic expressions exist for various characteristics of the Stable distribution (e.g., probability distribution function), with the Gaussian, Cauchy and Lévy distributions as special cases. While solutions for implementing these expressions have been proposed, care is required; the expressions are ill-behaved in different ways over some intervals of their parameter values.

Andrea has spent several years studying the Stable distribution analytically and was keen to create an implementation. My approach for complicated stuff is to find an existing implementation and adopt it. While everybody else worked their way through the copious papers that Andrea had brought along, I searched for existing implementations.

I found several implementations, but they all suffered from using approaches that delivered discontinuities and poor accuracies over some range of parameter values.

Eventually I got lucky and found a paper by Royuela-del-Val, Simmross-Wattenberg and Alberola-López, which described their implementation in C: Libstable (licensed under the GPL, perfect for SciPy); they also provided lots of replication material from their evaluation. An R package was available, but no Python support.

No other implementations were found. Team Stable laws decided to create a new implementation in Python and to create a Python module to interface to the C code in `libstable` (the bit I got to do). Two implementations would allow performance and accuracy to be compared (accuracy checks really need three implementations to get some idea of which might be incorrect, when output differs).

One small fix was needed to build `libstable` under OS X (change `Makefile` to link against `.so` library, rather than `.a`) and a different fix was needed to install the R package under OS X (R patch; Windows and generic Unix were fine).

Python’s ctypes module looked after loading the C shared library I built, along with converting the NumPy arrays. My PyStable module will not win any Python beauty contest, it is a means of supporting the comparison of multiple implementations.

Some progress was made towards creating a new implementation, more than 24 hours is obviously needed (`libstable` contains over 4,000 lines of code). I had my own problems with an exception being raised in calls to `stable_pdf`; `libstable` used the GNU Scientific Library and I tracked the problem down to a call into GSL, but did not get any further.

We all worked overnight, my first 24-hour hack in a very long time (I got about 4-hours sleep).

After Sunday lunch around 10 teams presented and after a quick deliberation, Team Stable laws were announced as the winners; yea!

Hopefully, over the coming weeks a usable implementation will come into being.

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## Influential philosophers of source code

April 19th, 2018 1 comment

Who is the most important/influential philosopher of source code? Source code, as far as I know, is not a subject that philosophers claim to be studying; but, the study of logic, language and the mind is the study of source code.

For many, Ludwig Wittgenstein would probably be the philosopher that springs to mind. Wittgenstein became famous as the world’s first Perl programmer, with statements such as: “If a lion could talk, we could not understand him.” and “Whereof one cannot speak, thereof one must be silent.”

Noam Chomsky, a linguist, might be another choice, based on his specification of the Chomsky hierarchy (which neatly categorizes grammars). But generative grammars (for which he is famous in linguistics) is about generating language, not understanding what has been said/written.

My choice for the most important/influential philosopher of source code is Paul Grice. A name, I suspect, that is new to most readers. The book to quote (and to read if you enjoy the kind of books philosophers write) is “Studies in the Way of Words”.

Grice’s maxims, provide a powerful model for human communication; the tldr:

• Maxim of quality: Try to make your contribution one that is true.
• Maxim of quantity: Make your contribution as informative as is required.
• Maxim of relation: Be relevant.

But source code is about human/computer communication, you say. Yes, but so many developers seem to behave as-if they were involved in human/human communication.

Source code rarely expresses what the developer means; source code is evidence of what the developer means.

The source code chapter of my empirical software engineering book is Gricean, with a Relevance theory accent.

More easily digestible books on Grice’s work (for me at least) are: “Relevance: Communication and Cognition” by Sperber and Wilson, and the more recent “Meaning and Relevance” by Wilson and Sperber.

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## The C++ committee has taken off its ball and chain

A step change in the approach to updates and additions to the C++ Standard occurred at the recent WG21 meeting, or rather a change that has been kind of going on for a few meetings has been documented and discussed. Two bullet points at the start of “C++ Stability, Velocity, and Deployment Plans [R2]”, grab reader’s attention:

● Is C++ a language of exciting new features?
● Is C++ a language known for great stability over a long period?

followed by the proposal (which was agreed at the meeting): “The Committee should be willing to consider the design / quality of proposals even if they may cause a change in behavior or failure to compile for existing code.”

We have had 30 years of C++/C compatibility (ok, there have been some nibbling around the edges over the last 15 years). A remarkable achievement, thanks to Bjarne Stroustrup over 30+ years and 64 full-week standards’ meetings (also, Tom Plum and Bill Plauger were engaged in shuttle diplomacy between WG14 and WG21).

The C/C++ superset/different issue has a long history.

In the late 1980s SC22 (the top-level ISO committee for programming languages) asked WG14 (the C committee) whether a standard should be created for C++, and if so did WG14 want to create it. WG14 considered the matter at its April 1989 meeting, and replied that in its view a standard for C++ was worth considering, but that the C committee were not the people to do it.

In 1990, SC22 started a study group to look into whether a working group for C++ should be created and in the U.S. X3 (the ANSI committee responsible for Information processing systems) set up X3J16. The showdown meeting of what would become WG21, was held in London, March 1992 (the only ISO C++ meeting I have attended).

The X3J16 people were in London for the ISO meeting, which was heated at times. The two public positions were: 1) work should start on a standard for C++, 2) C++ was not yet mature enough for work to start on a standard.

The, not so public, reason given for wanting to start work on a standard was to stop, or at least slow down, changes to the language. New releases, rumored and/or actual, of Cfront were frequent (in a pre-Internet time sense). Writing large applications in a version of C++ that was replaced with something sightly different six months later had developers in large companies pulling their hair out.

You might have thought that compiler vendors would be happy for the language to be changing on a regular basis; changes provide an incentive for users to pay for compiler upgrades. In practice the changes were so significant that major rework was needed by somebody who knew what they were doing, i.e., expensive people had to be paid; vendors were more used to putting effort into marketing minor updates. It was claimed that implementing a C++ compiler required seven times the effort of implementing a C compiler. I have no idea how true this claim might have been (it might have been one vendor’s approximate experience). In the 1980s everybody and his dog had their own C compiler and most of those who had tried, had run into a brick wall trying to implement a C++ compiler.

The stop/slow down changing C++ vs. let C++ “fulfill its destiny” (a rallying call from the AT&T rep, which the whole room cheered) finally got voted on; the study group became a WG (I cannot tell you the numbers; the meeting minutes are not online and I cannot find a paper copy {we had those until the mid/late-90s}).

The creation of WG21 did not have the intended effect (slowing down changes to the language); Stroustrup joined the committee and C++ evolution continued apace. However, from the developers’ perspective language change did slow down; Cfront changes stopped because its code was collapsing under its own evolutionary weight and usable C++ compilers became available from other vendors (in the early days, Zortech C++ was a major boost to the spread of usage).

The last WG21 meeting had 140 people on the attendance list; they were not all bored consultants looking for a creative outlet (i.e., exciting new features), but I’m sure many would be happy to drop the ball-and-chain (otherwise known as C compatibility).

I think there will be lots of proposals that will break C compatibility in one way or another and some will make it into a published standard. The claim will be that the changes will make life easier for future C++ developers (a claim made by proponents of every language, for which there is zero empirical evidence). The only way of finding out whether a change has long term benefit is to wait a long time and see what happens.

The interesting question is how C++ compiler vendors will react to breaking changes in the language standard. There are not many production compilers out there these days, i.e., not a lot of competition. What incentive does a compiler vendor have to release a version of their compiler that will likely break existing code? Compiler validation, against a standard, is now history.

If WG21 make too many breaking changes, they could find C++ vendors ignoring them and developers asking whether the ISO C++ standards’ committee is past its sell by date.

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## GDPR has a huge impact on empirical software engineering research

The EU’s General Data Protection Regulation (GDPR) is going to have a huge impact on empirical software engineering research. After 25 May 2018, analyzing source code will never be the same again.

I am not a lawyer and nothing qualifies me to talk about the GDPR.

People put their name in source code, bug tracking databases and discussion forums; this is personal identifying information.

Researchers use personal names to obtain information about a wide variety of activities, e.g., how much code did individuals write, how many bug reports did they process, contributions in discussions of one sort or another.

Open source licenses give others all kinds of rights (e.g., ability to use and modify source code), but they do not contain any provisions for processing personal data.

Adding a “I hereby give permission for anybody to process information about my name in any way they see fit.” clause to licenses is not going to help.

The GDPR requires (article 5: Principles relating to processing of personal data):

“Personal data shall be: … collected for specified, explicit and legitimate purposes and not further processed in a manner that is incompatible with those purposes;”

That is, personal data can only be processed for the specific reason it was collected, i.e., if you come up with another bright idea for analysis of data that has just been collected, it may be necessary to obtain consent, from those whose personal data it is, before trying out the bright idea.

It is not possible to obtain blanket permission (article 6, Lawfulness of processing):

“…the data subject has given consent to the processing of his or her personal data for one or more specific purposes;”, i.e., consent has to be obtained from the data subject for each specific purpose.

Github’s Global Privacy Practices shows that Github are intent on meeting the GDPR requirements, they include: “GitHub provides clear methods of unambiguous, informed consent at the time of data collection, when we do collect your personal data.”. Processing personal information, about an EU citizen, contained in source code appears to be a violation of Github’s terms of service.

The GDPR has many other requirements, e.g., right to obtain information on what information is held and right to be forgotten. But, the upfront killer is not being able to cheaply collect lots of code and then use personal information to help with the analysis.

There are exceptions for: Processing for archiving, scientific or historical research or statistical purposes. Can somebody who blogs and is writing a book claim to be doing scientific research? People who know more about these exceptions than me, tell me that there could be a fair amount of paperwork involved when making use of the exception, i.e., being able to show that privacy safeguards are in place.

Then, there is the issue of what constitutes personal information. Git’s hashing algorithm makes use of the committer’s name and/or email address. Is a git hash personal identifying information?

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## Reliability chapter added to “Empirical software engineering using R”

The Reliability chapter of my Empirical software engineering book has been added to the draft pdf (download here).

I have been working on this draft for four months and it still needs lots of work; time to move on and let it stew for a while. Part of the problem is lack of public data; cost and schedule overruns can be rather public (projects chapter), but reliability problems are easier to keep quiet.

Originally there was a chapter covering reliability and another one covering faults. As time passed, these merged into one. The material kept evaporating in front of my eyes (around a third of the initial draft, collected over the years, was deleted); I have already written about why most fault prediction research is a waste of time. If it had not been for Rome I would not have had much to write about.

Perhaps what will jump out at people most, is that I distinguish between mistakes in code and what I call a fault experience. A `fault_experience=mistake_in_code + particular_input`. Most fault researchers have been completely ignoring half of what goes into every fault experience, the input profile (if the user does not notice a fault, I do not consider it experienced) . It’s incredibly difficult to figure out anything about the input profile, so it has been quietly ignored (one of the reasons why research papers on reported faults are such a waste of time).

I’m also missing an ‘interesting’ figure on the opening page of the chapter. Suggestions welcome.

I have not said much about source code characteristics. There is a chapter covering source code, perhaps some of this material will migrate to reliability.

All sorts of interesting bits and pieces have been added to earlier chapters. Ecosystems keeps growing and in years to come somebody will write a multi-volume tomb on software ecosystems.

I have been promised all sorts of data. Hopefully some of it will arrive.

As always, if you know of any interesting software engineering data, please tell me.

Source code chapter next.

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