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Practical ecosystem books for software engineers

Derek Jones from The Shape of Code

So you have read my (draft) book on evidence-based software engineering and want to learn more about ecosystems. What books do I suggest?

Biologists have been studying ecosystems for a long time, and more recently social scientists have been investigating cultural ecosystems. Many of the books written in these fields are oriented towards solving differential equations and are rather subject specific.

The study of software ecosystems has been something of a niche topic for a long time. Problems for researchers have included gaining access to ecosystems and the seeming proliferation of distinct ecosystems. The state of ecosystem research in software engineering is rudimentary; historians are starting to piece together what has happened.

Most software ecosystems are not even close to being in what might be considered a steady state. Eventually most software will be really old, and this will be considered normal (“Shock Of The Old: Technology and Global History since 1900″ by Edgerton; newness is a marketing ploy to get people to buy stuff). In the meantime, I have concentrated on the study of ecosystems in a state of change.

Understanding ecosystems is about understanding how the interaction of participant’s motivation, evolves the environment in which they operate.

“Modern Principles of Economics” by Cowen and Tabarrok, is a very readable introduction to economics. Economics might be thought of as a study of the consequences of optimizing the motivation of maximizing return on investment. “Principles of Corporate Finance” by Brealey and Myers, focuses on the topic in its title.

“The Control Revolution: Technological and Economic Origins of the Information Society” by Beniger: the ecosystems in which software ecosystems coexist and their motivations.

“Evolutionary dynamics: exploring the equations of life” by Nowak, is a readable mathematical introduction to the subject given in the title.

“Mathematical Models of Social Evolution: A Guide for the Perplexed” by McElreath and Boyd, is another readable mathematical introduction, but focusing on social evolution.

“Social Learning: An Introduction to Mechanisms, Methods, and Models” by Hoppitt and Laland: developers learn from each other and from their own experience. What are the trade-offs for the viability of an ecosystem that preferentially contains people with specific ways of learning?

“Robustness and evolvability in living systems” by Wagner, survival analysis of systems built from components (DNA in this case). Rather specialised.

Books with a connection to technology ecosystems.

“Increasing returns and path dependence in the economy” by Arthur, is now a classic, containing all the basic ideas.

“The red queen among organizations” by Barnett, includes a chapter on computer manufacturers (has promised me data, but busy right now).

“Information Foraging Theory: Adaptive Interaction with Information” by Pirolli, is an application of ecosystem know-how, i.e., how best to find information within a given environment. Rather specialised.

“How Buildings Learn: What Happens After They’re Built” by Brand, yes building are changed just like software and the changes are just as messy and expensive.

Several good books have probably been omitted, because I failed to spot them sitting on the shelf. Suggestions for books covering topics I have missed welcome, or your own preferences.

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Practical psychology books for software engineers

Derek Jones from The Shape of Code

So you have read my (draft) book on evidence-based software engineering and want to learn more about human psychology. What books do I suggest?

I wrote a book about C that attempted to use results from cognitive psychology to understand developer characteristics. This work dates from around 2000, and some of my book choices may have been different, had I studied the subject 10 years later. Another consequence is that this list is very weak on social psychology.

I own all the following books, but it may have been a few years since I last took them off the shelf.

There are two very good books providing a broad introduction: “Cognitive psychology and its implications” by Anderson, and “Cognitive psychology: A student’s handbook” by Eysenck and Keane. They have both been through many editions, and buying a copy that is a few editions earlier than current, saves money for little loss of content.

“Engineering psychology and human performance” by Wickens and Hollands, is a general introduction oriented towards stuff that engineering requires people to do.

Brain functioning: “Reading in the brain” by Dehaene (a bit harder going than “The number sense”). For those who want to get down among the neurons “Biological psychology” by Kalat.

Consciouness: This issue always comes up, so let’s kill it here and now: “The illusion of conscious will” by Wegner, and “The mind is flat” by Chater.

Decision making: What is the difference between decision making and reasoning? In psychology those with a practical orientation study decision making, while those into mathematical logic study reasoning. “Rational choice in an uncertain world” by Hastie and Dawes, is a general introduction; “The adaptive decision maker” by Payne, Bettman and Johnson, is a readable discussion of decision making models. “Judgment under Uncertainty: Heuristics and Biases” by Kahneman, Slovic and Tversky, is a famous collection of papers that kick started the field at the start of the 1980s.

Evolutionary psychology: “Human evolutionary psychology” by Barrett, Dunbar and Lycett. How did we get to be the way we are? Watch out for the hand waving (bones can be dug up for study, but not the software of our mind), but it weaves a coherent’ish story. If you want to go deeper, “The Adapted Mind: Evolutionary Psychology and the Generation of Culture” by Barkow, Tooby and Cosmides, is a collection of papers that took the world by storm at the start of the 1990s.

Language: “The psychology of language” by Harley, is the book to read on psycholinguistics; it is engrossing (although I have not read the latest edition).

Memory: I have almost a dozen books discussing memory. What these say is that there are a collection of memory systems having various characteristics; which is what the chapters in the general coverage books say.

Modeling: So you want to model the human brain. ACT-R is the market leader in general cognitive modeling. “Bayesian cognitive modeling” by Lee and Wagenmakers, is a good introduction for those who prefer a more abstract approach (“Computational modeling of cognition” by Farrell and Lewandowsky, is a big disappointment {they have written some great papers} and best avoided).

Reasoning: The study of reasoning is something of a backwater in psychology. Early experiments showed that people did not reason according to the rules of mathematical logic, and this was treated as a serious fault (whose fault it was, shifted around). Eventually most researchers realised that the purpose of reasoning was to aid survival and reproduction, not following the recently (100 years or so) invented rules of mathematical logic (a few die-hards continue to cling to the belief that human reasoning has a strong connection to mathematical logic, e.g., Evans and Johnson-Laird; I have nearly all their books, but have not inflicted them on the local charity shop yet). Gigerenzer has written several good books: “Adaptive thinking: Rationality in the real world” is a readable introduction, also “Simple heuristics that make us smart”.

Social psychology: “Social learning” by Hoppitt and Laland, analyzes the advantages and disadvantages of social learning; “The Secret of Our Success: How Culture Is Driving Human Evolution, Domesticating Our Species, and Making Us Smarter” by Henrich, is a more populist book (by a leader in the field).

Vision: “Visual intelligence” by Hoffman is a readable introduction to how we go about interpreting the photons entering our eyes, while “Graph design for the eye and mind” by Kosslyn is a rule based guide to visual presentation. “Vision science: Photons to phenomenology” by Palmer, for those who are really keen.

Several good books have probably been omitted, because I failed to spot them sitting on the shelf. Suggestions for books covering topics I have missed welcome, or your own preferences.

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Practical statistics books for software engineers

Derek Jones from The Shape of Code

So you have read my (draft) book on evidence-based software engineering and want to learn more about the statistical techniques used, but are not interested lots of detailed mathematics. What books do I suggest?

All the following books are sitting on the shelf next to where I write (not that they get read that much these days).

Before I took the training wheels off my R usage, my general go to book was (I still look at it from time to time): “The R Book” by Crawley, second edition; “R in Action” by Kabacoff is a good general read.

In alphabetical subject order:

Categorical data: “Categorical Data Analysis” by Agresti, the third edition is a weighty tomb (in content and heaviness). Plenty of maths+example; more of a reference.

Compositional data: “Analyzing compositional data with R” by van den Boogaart and Tolosana-Delgado, is more or less the only book of its kind. Thankfully, it is quite good.

Count data: “Modeling count data” by Hilbe, may be more than you want to know about count data. Readable.

Circular data: “Circular statistics in R” by Pewsey, Neuhauser and Ruxton, is the only non-pure theory book available. The material seems to be there, but is brief.

Experiments: “Design and analysis of experiments” by Montgomery.

General: “Applied linear statistical models” by Kutner, Nachtsheim, Neter and Li, covers a wide range of topics (including experiments) using a basic level of mathematics.

Mixed-effects models: “Mixed-effects models in S and S-plus” by Pinheiro and Bates, is probably the book I prefer; “Mixed effects models and extensions in ecology with R” by Zuur, Ieno, Walker, Saveliev and Smith, is another view on an involved topic (plus lots of ecological examples).

Modeling: “Statistical rethinking” by McElreath, is full of interesting modeling ideas, using R and Stan. I wish I had some data to try out some of these ideas.

Regression analysis: “Applied Regression Analysis and Generalized Linear Models” by Fox, now in its third edition (I also have the second edition). I found this the most useful book, of those available, for a more detailed discussion of regression analysis. Some people like “Regression modeling strategies” by Harrell, but this does not appeal to me.

Survival analysis: “Introducing survival and event history analysis” by Mills, is a readable introduction covering everything; “Survival analysis” by Kleinbaum and Klein, is full of insights but more of a book to dip into.

Time series: The two ok books are: “Time series analysis and its application: with R examples” by Shumway and Stoffler, contains more theory, while “Time series analysis: with applications in R” by Cryer and Chan, contains more R code.

There are lots of other R/statistics books on my shelves (just found out I have 31 of Springer’s R books), some ok, some not so. I have a few ‘programming in R’ style books; if you are a software developer, R the language is trivial to learn (its library is another matter).

Suggestions for books covering topics I have missed welcome, or your own preferences (as a software developer).

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The first commercially available (claimed) verified compiler

Derek Jones from The Shape of Code

Yesterday, I read a paper containing a new claim by some of those involved with CompCert (yes, they of soap powder advertising fame): “CompCert is the first commercially available optimizing compiler that is formally verified, using machine assisted mathematical proofs, to be exempt from miscompilation”.

First commercially available; really? Surely there are earlier claims of verified compilers being commercial availability. Note, I’m saying claims; bits of the CompCert compiler have involved mathematical proofs (i.e., code generation), so I’m considering earlier claims having at least the level of intellectual honesty used in some CompCert papers (a very low bar).

What does commercially available mean? The CompCert system is open source, so I guess it’s commercially available via free downloading (the paper does not define the term).

Computational Logic, Inc is the name that springs to mind, when thinking of commercial and formal verification. They were active from 1983 to 1997, and published some very interesting technical reports about their work (sadly there are gaps in the archive). One project was A Mechanically Verified Code Generator (in 1989) and their Gypsy system (a Pascal-like language+IDE) provided an environment for doing proofs of programs (I cannot find any reports online). Piton was a high-level assembler and there was a mechanically verified implementation (in 1988).

There is the Danish work on the formal specification of the code generators for their Ada compiler (while there was a formal specification of the Ada semantics in VDM, code generators tend to be much simpler beasts, i.e., a lot less work is needed in formal verification). The paper I have is: “Retargeting and rehosting the DDC Ada compiler system: A case study – the Honeywell DPS 6″ by Clemmensen, from 1986 (cannot find an online copy). This Ada compiler was used by various hardware manufacturers, so it was definitely commercially available for (lots of) money.

Are then there any earlier verified compilers with a commercial connection? There is A PRACTICAL FORMAL SEMANTIC DEFINITION AND VERIFICATION SYSTEM FOR TYPED LISP, from 1976, which has “… has proved a number of interesting, non-trivial theorems including the total correctness of an algorithm which sorts by successive merging, the total correctness of the McCarthy-Painter compiler for expressions, …” (which sounds like a code generator, or part of one, to me).

Francis Morris’s thesis, from 1972, proves the correctness of compilers for three languages (each language contained a single feature) and discusses how these features may be combined into a more “realistic” language. No mention of commercial availability, but I cannot see the demand being that great.

The definition of PL/1 was written in VDM, a formal language. PL/1 is a huge language and there were lots of subsets. Were there any claims of formal verification of a subset compiler for PL/1? I have had little contact with the PL/1 world, so am not in a good position to know. Anybody?

Over to you dear reader. Are there any earlier claims of verified compilers and commercial availability?

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Publishing information on project progress: will it impact delivery?

Derek Jones from The Shape of Code

Numbers for delivery date and cost estimates, for a software project, depend on who you ask (the same is probably true for other kinds of projects). The people actually doing the work are likely to have the most accurate information, but their estimates can still be wildly optimistic. The managers of the people doing the work have to plan (i.e., make worst/best case estimates) and deal with people outside the team (i.e., sell the project to those paying for it); planning requires knowledge of where things are and where they need to be, while selling requires being flexible with numbers.

A few weeks ago I was at a hackathon organized by the people behind the Project Data and Analytics meetup. The organizers (Martin Paver & co.) had obtained some very interesting project related data sets. I worked on the Australian ICT dashboard data.

The Australian ICT dashboard data was courtesy of the Queensland state government, which has a publicly available dashboard listing digital project expenditure; the Victorian state government also has a dashboard listing ICT expenditure. James Smith has been collecting this data on a monthly basis.

What information might meaningfully be extracted from monthly estimates of project delivery dates and costs?

If you were running one of these projects, and had to provide monthly figures, what strategy would you use to select the numbers? Obviously keep quiet about internal changes for as long as possible (today’s reduction can be used to offset a later increase, or vice versa). If the client requests changes which impact date/cost, then obviously update the numbers immediately; the answer to the question about why the numbers changed is that, “we are responding to client requests” (i.e., we would otherwise still be on track to meet the original end-points).

What is the intended purpose of publishing this information? Is it simply a case of the public getting fed up with overruns, with publishing monthly numbers is seen as a solution?

What impact could monthly publication have? Will clients think twice before requesting an enhancement, fearing public push back? Will companies doing the work make more reliable estimates, or work harder?

Project delivery dates/costs change because new functionality/work-to-do is discovered, because the appropriate staff could not be hired and other assorted unknown knowns and unknowns.

Who is looking at this data (apart from half a dozen people at a hackathon on the other side of the world)?

Data on specific projects can only be interpreted in the context of that project. There is some interesting research to be done on the impact of public availability on client and vendor reporting behavior.

Will publication have an impact on performance? One way to get some idea is to run an A/B experiment. Some projects have their data made public, others don’t. Wait a few years, and compare project performance for the two publication regimes.

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Statistical techniques not needed to analyze software engineering data

Derek Jones from The Shape of Code

One of the methods I used to try to work out what statistical techniques were likely to be useful to software developers, was to try to apply techniques that were useful in other areas. Of course, applying techniques requires the appropriate data to apply them to.

Extreme value statistics are used to spot patterns in rare events, e.g., frequency of rivers over spilling their banks and causing extensive flooding. I have tried and failed to find any data where Extreme value theory might be applicable. There probably is some such data, somewhere.

The fact that I have spent a lot of time looking for data and failed to find particular kinds of data, suggests that occurrences are rare. If data needing a particular kind of analysis technique is rare, there is no point including a discussion of the technique in a book aimed at providing general coverage of material.

I have spent some time looking for data drawn from a zero-inflated Poisson distribution. Readers are unlikely to have ever heard of this and might well ask why I would be interested in such an obscure distribution. Well, zero-truncated Poisson distributions crop up regularly (the Poisson distribution applies to count data that starts at zero, when count data starts at one the zeroes are said to be truncated and the Poisson distribution has to be offset to adjust for this). There is a certain symmetry to zero-truncated/inflated (although the mathematics involved is completely different), plus there is probably a sunk cost effect (i.e., I have spent time learning about them, I am going to find the data).

I spotted a plot in a paper investigating record data structure usage in Racket, that looked like it might be well fitted by a zero-inflated Poisson distribution. Tobias Pape kindly sent me the data (number of record data structures having a given size), which I then failed miserably to fit to any kind of Poisson related distribution; see plot below; data points along red line through the plus symbols (code+data):

Number of Racket record data structures having a given size.

I can only imagine what the authors thought of my reason for wanting the data (I made data requests to a few other researchers for similar reasons; and again I failed to fit the desired distribution).

I had expected to make more use of time series analysis; but, it has just not been that applicable.

Machine learning is useful for publishing papers, but understanding what is going on is the subject of my book, not building black boxes to make predictions.

It is possible that researchers are not publishing work relating to data that requires statistical techniques I have not used, because they don’t know how to analyze the data or the data is too hard to collect. Inability to use the correct techniques to analyze data is rarely a reason for not publishing a paper. Data being too hard to collect is very believable, as-is the data rarely occurring in software engineering related work.

There are statistical tests I have intentionally ignored, the Mann–Whitney U test (aka, the Wilcoxon rank-sum test) and the t-test probably being the most well-known. These tests became obsolete once computers became generally available. If you are ever stuck on a desert island without a computer, these are the statistical tests you will have to use.

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Students vs. professionals in software engineering experiments

Derek Jones from The Shape of Code

Experiments are an essential component of any engineering discipline. When the experiments involve people, as subjects in the experiment, it is crucial that the subjects are representative of the population of interest.

Academic researchers have easy access to students, but find it difficult to recruit professional developers, as subjects.

If the intent is to generalize the results of an experiment to the population of students, then using student as subjects sounds reasonable.

If the intent is to generalize the results of an experiment to the population of professional software developers, then using student as subjects is questionable.

What it is about students that makes them likely to be very poor subjects, to use in experiments designed to learn about the behavior and performance of professional software developers?

The difference between students and professionals is practice and experience. Professionals have spent many thousands of hours writing code, attending meetings discussing the development of software; they have many more experiences of the activities that occur during software development.

The hours of practice reading and writing code gives professional developers a fluency that enables them to concentrate on the problem being solved, not on technical coding details. Yes, there are students who have this level of fluency, but most have not spent the many hours of practice needed to achieve it.

Experience gives professional developers insight into what is unlikely to work and what may work. Without experience students have no way of evaluating the first idea that pops into their head, or a situation presented to them in an experiment.

People working in industry are well aware of the difference between students and professional developers. Every year a fresh batch of graduates start work in industry. The difference between a new graduate and one with a few years experience is apparent for all to see. And no, Masters and PhD students are often not much better and in some cases worse (their prolonged sojourn in academia means that have had more opportunity to pick up impractical habits).

It’s no wonder that people in industry laugh when they hear about the results from experiments based on student subjects.

Just because somebody has “software development” in their job title does not automatically make they an appropriate subject for an experiment targeting professional developers. There are plenty of managers with people skills and minimal technical skills (sub-student level in some cases)

In the software related experiments I have run, subjects were asked how many lines of code they had read/written. The low values started at 25,000 lines. The intent was for the results of the experiments to be generalized to the population of people who regularly wrote code.

Psychology journals are filled with experimental papers that used students as subjects. The intent is to generalize the results to the general population. It has been argued that students are not representative of the general population in that they have spent more time reading, writing and reasoning than most people. These subjects have been labeled as WEIRD.

I spend a lot of time reading software engineering papers. If a paper involves human subjects, the first thing I do is find out whether the subjects were students (usual) or professional developers (not common). Authors sometimes put effort into dressing up their student subjects as having professional experience (perhaps some of them have spent a year or two in industry, but talking to the authors often reveals that the professional experience was tutoring other students), others say almost nothing about the identity of the subjects. Papers describing experiments using professional developers, trumpet this fact in the abstract and throughout the paper.

I usually delete any paper using student subjects, some of the better ones are kept in a subdirectory called students.

Software engineering researchers are currently going through another bout of hand wringing over the use of student subjects. One paper makes the point that a student based experiment is a good way of validating an experiment that will later involve professional developers. This is a good point, but ignored the problem that researchers rarely move on to using professional subjects; many researchers only ever intend to run student-based experiments. Also, they publish the results from the student based experiment, which are at best misleading (but academics get credit for publishing papers, not for the content of the papers).

Researchers are complaining that reviews are rejecting their papers on student based experiments. I’m pleased to hear that reviewers are rejecting these papers.

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The best or most compiler writers born in February?

Derek Jones from The Shape of Code

Some years ago, now, I ran a poll asking about readers’ month of birth and whether they had worked on a compiler. One hypothesis was that the best compiler writers are born in February, an alternative hypothesis is that most compiler writers are born in February.

I have finally gotten around to analyzing the data and below is the Rose diagram for the 82, out of 132 responses, compiler writers (the green arrow shows the direction and magnitude of the mean; code+data):

Rose diagram of birth month of compiler writers

At 15% of responses, February is the most common month for compiler writer birthdays. The percentage increases to 16%, if weighted by the number of births in each month.

So there you have it, the hypothesis that most compiler writers are born in February is rejected, leaving the hypothesis that the best compiler writers are born in February. How could this not be true :-)

What about the birth month of readers who are not compiler writers? While the mean direction and length are more-or-less the same, for the two populations, the Rose diagram shows that the shape of the distributions are different:

Rose diagram of birth month of non-compiler writers

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Facebook’s Big Code Summit

Derek Jones from The Shape of Code

I was at Facebook’s first Big Code Summit on Monday and Tuesday (I say the first, because I hope there is another one next year).

The talks all involved machine learning (to be expected, given the Big Code in the event’s title). Normally I ignore papers on machine learning in software engineering, but understanding code is hard and we don’t know much about it. As I keep telling anyone who will listen, machine learning is the tool to use when you don’t know what you are doing (provided you have enough data).

People have been learning code patterns for some time now, suggesting applications in code completion in the IDE and finding suspicious API sequences (e.g., a missing call). This is one area where machine learning is a natural solution: nobody has the time to write down all the common patterns, for all the common languages, and APIs are constantly changing. It makes no sense to solve this problem manually.

So what was new and/or interesting?

We got new and very interesting in the first talk, when Eran Yahav presented his group’s work on cod2vec, the paper was interesting, but the demo had the wow factor.

I have not made up my mind about Michael Pradel‘s proposal for learning new coding rule checks. These rules are often created by people, but people with the necessary skill are thin on the ground. Machine learning requires something to learn from, how could coding rules be created this way. Michael’s group is working on a system where developers create the positive and negative cases and a machine learner figures out rules from these examples. Would the creation of these positive/negative examples prove to be just as hard as writing rules? I was not convinced that such an approach was practical, but if somebody wants to try it out, why not.

I found Xinyun Chen‘s talk interesting, but then I’ve written lots of parsers, and automatically figuring out how to parse a language from examples will always get my attention. A few people in the audience thought that a better solution was typing in a grammar and parsing the ‘usual’ way. This approach assumes a grammar exists, can be strong-armed into a form that is practical to embed in a parser (requiring somebody skilled in the necessary black arts), to produce a system that will only process complete translation units (or whatever the language calls a unit of translation).

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Adding a new scalar type to C

Derek Jones from The Shape of Code

I think the time has arrived for a new scalar type in C, which for want of a better name I shall call the compendium type.

On today’s processors a compendium type behaves a lot like an integer type, except that nobody really wants to include it in the list of supported integer types, e.g., 128-bit scalars.

Why is a new scalar type needed? The Standard supports extended integer types, why not treat a scalar object that supports integer arithmetic as an integer type?

The C Standard says (section 6.2.5 Types):
“There are five standard signed integer types, designated as signed char, short int, int, long int, and long long int. (These and other types may be designated in several additional ways, as described in 6.7.2.) There may also be implementation-defined extended signed integer types.38) The standard and extended signed integer types are collectively called signed integer types.39)”

There is corresponding wording for unsigned integer types.

The standard header allows implementations to define a whole menagerie of integer types: section 7.20.1.1 Exact-width integer types
“The typedef name intN_t designates a signed integer type with width N, no padding bits, and a two’s complement representation. Thus, int8_t denotes such a signed integer type with a width of exactly 8 bits.”

This all sounds very feasible, but there is a catch. The Standard defines a greatest-width integer type, section 7.20.1.5 Greatest-width integer types
“The following type designates a signed integer type capable of representing any value of any signed integer type:
intmax_t”

and various library functions have an argument type intmax_t (there is also an uintmax_t).

An ‘extra-large’ integer type is not something that can just sit there, in the list of available integer types, waiting to be used. Preprocessor arithmetic and a variety of library are based around the type intmax_t. An extra-large integer type would have a very visible impact on all developers, many of whom would want to ignore it.

GCC supports 128-bit integers, e.g., __int128. But some magic pixie dust is involved, this type has no connection with intmax_t.

What do developers do with these 128- and 256-bit scalar objects? Evaluating graphics algorithms, hashes and cryptographic calculations are obvious candidates; yes, perhaps even calculations involving integers that require this many bits. I have not seen any analysis of the uses of this kind of wide-integer-like type.

Extra-wide scalar types have a variety of uses and the term compendium type, captures this. Hardware support for such extra-width types is growing, with vendors looking to fill major niches.

Contorting existing wording, in the Standard, so accommodate these extra-wide types within the existing integer type machinery is a short term solution. Work on the upcoming revision of the C Standard should either do nothing and allow vendors to take the approach currently used by GCC, or create a new scalar type (perhaps using a TR).