Survival rate of WG21 meeting attendance
WG21, the C++ Standards committee, has a very active membership, with lots of people attending the regular meetings; there are three or four meetings a year, with an average meeting attendance of 67 (between 2004 and 2016).
The minutes of WG21 meetings list those who attend, and a while ago I downloaded these for meetings between 2004 and 2016. Last night I scraped the data and cleaned it up (or at least the attendee names).
WG21 had its first meeting in 1992, and continues to have meetings (eleven physical meetings at the time or writing). This means the data is both left and right censored; known as interval censored. Some people will have attended many meetings before the scraped data starts, and some people listed in the data may not have attended another meeting since.
What can we say about the survival rate of a person being a WG21 attendee in the future, e.g., what is the probability they will attend another meeting?
Most regular attendees are likely to miss a meeting every now and again (six people attended all 30 meetings in the dataset, with 22 attending more than 25), and I assumed that anybody who attended a meeting after 1 January 2015 was still attending. Various techniques are available to estimate the likelihood that known attendees were attending meetings prior to those in the dataset (I’m going with what ever R’s survival
package does). The default behavior of R’s Surv
function is to handle right censoring, the common case. Extra arguments are needed to handle interval censored data, and I think I got these right (I had to cast a logical argument to numeric for some reason; see code+data).
The survival curves in days since 1 Jan 2004, and meetings based on the first meeting in 2004, with 95% confidence bounds, look like this:
I was expecting a sharper initial reduction, and perhaps wider confidence bounds. Of the 374 people listed as attending a meeting, 177 (47%) only appear once and 36 (10%) appear twice; there is a long tail, with 1.6% appearing at every meeting. But what do I know, my experience of interval censored data is rather limited.
The half-life of attendance is 9 to 10 years, suspiciously close to the interval of the data. Perhaps a reader will scrape the minutes from earlier meetings 🙂
Within the time interval of the data, new revisions of the C++ standard occurred in 20072011 and 2014; there had also been a new release in 2003, and one was being worked on for 2017. I know some people stop attending meetings after a major milestone, such as a new standard being published. A fancier analysis would investigate the impact of standards being published on meeting attendance.
People also change jobs. Do WG21 attendees change jobs to ones that also require/allow them to attend WG21 meetings? The attendee’s company is often listed in the minutes (and is in the data). Something for intrepid readers to investigate.
Christmas books for 2020
A very late post on the interesting books I read this year (only one of which was actually published in 2020). As always the list is short because I did not read many books and/or there is lots of nonsense out there, but this year I have the new excuses of not being able to spend much time on trains and having my own book to finally complete.
I have already reviewed The Weirdest People in the World: How the West Became Psychologically Peculiar and Particularly Prosperous, and it is the must-read of 2020 (after my book, of course :-).
The True Believer by Eric Hoffer. This small, short book provides lots of interesting insights into the motivational factors involved in joining/following/leaving mass movements. Possible connections to software engineering might appear somewhat tenuous, but bits and pieces keep bouncing around my head. There are clearer connections to movements going mainstream this year.
The following two books came from asking what-if questions about the future of software engineering. The books I read suggesting utopian futures did not ring true.
“Money and Motivation: Analysis of Incentives in Industry” by William Whyte provides lots of first-hand experience of worker motivation on the shop floor, along with worker response to management incentives (from the pre-automation 1940s and 1950s). Developer productivity is a common theme in discussions I have around evidence-based software engineering, and this book illustrates the tangled mess that occurs when management and worker aims are not aligned. It is easy to imagine the factory-floor events described playing out in web design companies, with some web-page metric used by management as a proxy for developer productivity.
Labor and Monopoly Capital: The Degradation of Work in the Twentieth Century by Harry Braverman, to quote from Wikipedia, is an “… examination the nature of ‘skill’ and the finding that there was a decline in the use of skilled labor as a result of managerial strategies of workplace control.” It may also have discussed management assault of blue-collar labor under capitalism, but I skipped the obviously political stuff. Management do want to deskill software development, if only because it makes it easier to find staff, with the added benefit that the larger pool of less skilled staff increases management control, e.g., low skilled developers knowing they can be easily replaced.
Software research is 200 years behind biology research
Evidence-based software research requires access to data, and Github has become the primary source of raw material for many (most?) researchers.
Parallels are starting to emerge between today’s researchers exploring Github and biologists exploring nature centuries ago.
Centuries ago scientific expeditions undertook difficult and hazardous journeys to various parts of the world, collecting and returning with many specimens which were housed and displayed in museums and botanical gardens. Researchers could then visit the museums and botanical gardens to study these specimens, without leaving the comforts of their home country. What is missing from these studies of collected specimens is information on the habitat in which they lived.
Github is a living museum of specimens that today’s researchers can study without leaving the comforts of their research environment. What is missing from these studies of collected specimens is information on the habitat in which the software was created.
Github researchers are starting the process of identifying and classifying specimens into species types, based on their defining characteristics, much like the botanist Carl_Linnaeus identified stamens as one of the defining characteristics of flowering plants. Some of the published work reads like the authors did some measurements, spotted some differences, and then invented a plausible story around what they had found. As a sometime inhabitant of this glasshouse I will refrain from throwing stones.
Zoologists study the animal kingdom, and entomologists specialize in the insect world, e.g., studying Butterflys. What name might be given to researchers who study software source code, and will there be specialists, e.g., those who study cryptocurrency projects?
The ecological definition of a biome, as the community of plants and animals that have common characteristics for the environment they exist in, maps to the end-user use of software systems. There does not appear to be a generic name for people who study the growth of plants and animals (or at least I cannot think of one).
There is only so much useful information that can be learned from studying specimens in museums, no matter how up to date the specimens are.
Studying the development and maintenance of software systems in the wild (i.e., dealing with the people who do it), requires researchers to forsake their creature comforts and undertake difficult and hazardous journeys into industry. While they are unlikely to experience any physical harm, there is a real risk that their egos will be seriously bruised.
I want to do what I can to prevent evidence-based software engineering from just being about mining Github. So I have a new policy for dealing with PhD/MSc student email requests for data (previously I did my best to point them at the data they sought). From now on, I will tell students that they need to behave like real researchers (e.g., Charles Darwin) who study software development in the wild. Charles Darwin is a great role model who should appeal to their sense of adventure (alternative suggestions welcome).
What software engineering data have I collected on subject X?
While it’s great that so much data was uncovered during the writing of the Evidence-based software engineering book, trying to locate data on a particular topic can be convoluted (not least because there might not be any). There are three sources of information about the data:
- the paper(s) written by the researchers who collected the data,
- my analysis and/or discussion of the data (which is frequently different from the original researchers),
- the column names in the csv file, i.e., data is often available which neither the researchers nor I discuss.
At the beginning I expected there to be at most a few hundred datasets; easy enough to remember what they are about. While searching for some data, one day, I realised that relying on memory was not a good idea (it was never a good idea), and started including data identification tags in every R file (of which there are currently 980+). This week has been spent improving tag consistency and generally tidying them up.
How might data identification information be extracted from the paper that was the original source of the data (other than reading the paper)?
Named-entity recognition, NER, is a possible starting point; after all, the data has names associated with it.
Tools are available for extracting text from pdf file, and 10-lines of Python later we have a list of named entities:
import spacy # Load English tokenizer, tagger, parser, NER and word vectors nlp = spacy.load("en_core_web_sm") file_name = 'eseur.txt' soft_eng_text = open(file_name).read() soft_eng_doc = nlp(soft_eng_text) for ent in soft_eng_doc.ents: print(ent.text, ent.start_char, ent.end_char, ent.label_, spacy.explain(ent.label_)) |
The catch is that en_core_web_sm is a general model for English, and is not software engineering specific, i.e., the returned named entities are not that good (from a software perspective).
An application domain language model is likely to perform much better than a general English model. While there are some application domain models available for spaCy (e.g., biochemistry), and application datasets, I could not find any spaCy models for software engineering (I did find an interesting word2vec model trained on Stackoverflow posts, which would be great for comparing documents, but not what I was after).
While it’s easy to train a spaCy NER model, the time-consuming bit is collecting and cleaning the text needed. I have plenty of other things to keep me busy. But this would be a great project for somebody wanting to learn spaCy and natural language processing 🙂
What information is contained in the undiscussed data columns? Or, from the practical point of view, what information can be extracted from these columns without too much effort?
The number of columns in a csv file is an indicator of the number of different kinds of information that might be present. If a csv is used in the analysis of X, and it contains lots of columns (say more than half-a-dozen), then it might be assumed that it contains more data relating to X.
Column names are not always suggestive of the information they contain, but might be of some use.
Many of the csv files contain just a few rows/columns. A list of csv files that contain lots of data would narrow down the search, at least for those looking for lots of data.
Another possibility is to group csv files by potential use of data, e.g., estimating, benchmarking, testing, etc.
More data is going to become available, and grouping by potential use has the advantage that it is easier to track the availability of new data that may supersede older data (that may contain few entries or apply to circumstances that no longer exist)
My current techniques for locating data on a given subject is either remembering the shape of a particular plot (and trying to find it), or using the pdf reader’s search function to locate likely words and phrases (and then look at the plots and citations).
Suggestions for searching or labelling the data, that don’t require lots of effort, welcome.
Researching programming languages
What useful things might be learned from evidence-based research into programming languages?
A common answer is researching how to design a programming language having a collection of desirable characteristics; with desirable characteristics including one or more of: supporting the creation of reliable, maintainable, readable, code, or being easy to learn, or easy to understand, etc.
Building a theory of, say, code readability is an iterative process. A theory is proposed, experiments are run, results are analysed; rinse and repeat until a theory having a good enough match to human behavior is found. One iteration will take many years: once a theory is proposed, an implementation has to be built, developers have to learn it, and spend lots of time using it to enable longer term readability data to be obtained. This iterative process is likely to take many decades.
Running one iteration will require 100+ developers using the language over several years. Why 100+? Lots of subjects are needed to obtain statistically meaningful results, people differ in their characteristics and previous software experience, and some will drop out of the experiment. Just one iteration is going to cost a lot of money.
If researchers do succeed in being funded and eventually discovering some good enough theories, will there be a mass migration of developers to using languages based on the results of the research findings? The huge investment in existing languages (both in terms of existing code and developer know-how) means that to stand any chance of being widely adopted these new language(s) are going to have to deliver a substantial benefit.
I don’t see a high cost multi-decade research project being funded, and based on the performance improvements seen in studies of programming constructs I don’t see the benefits being that great (benefits in use of particular constructs may be large, but I don’t see an overall factor of two improvement).
I think that creating new programming languages will continue to be a popular activity (it is vanity research), and I’m sure that the creators of these languages will continue to claim that their language has some collection of desirable characteristics without any evidence.
What programming research might be useful and practical to do?
One potentially practical and useful question is the lifecycle of programming languages. Where the components of the lifecycle includes developers who can code in the language, source code written in the language, and companies dependent on programs written in the language (who are therefore interested in hiring people fluent in the language).
Many languages come and go without many people noticing, a few become popular for a few years, and a handful continue to be widely used over decades. What are the stages of life for a programming language, what factors have the largest influence on how widely a language is used, and for how long it continues to be used?
Sixty years worth of data is waiting to be collected and collated; enough to keep researchers busy for many years.
The uses of a lifecycle model, that I can thinkk of, all involve the future of a language, e.g., how much of a future does it have and how might it be extended.
Some recent work looking at the rate of adoption of new language features includes: On the adoption, usage and evolution of Kotlin Features on Android development, and Understanding the use of lambda expressions in Java; also see section 7.3.1 of Evidence-based software engineering.
Evidence-based software engineering: book released
My book, Evidence-based software engineering, is now available; the pdf can be downloaded here, here and here, plus all the code+data. Report any issues here. I’m investigating the possibility of a printed version. Mobile friendly pdf (layout shaky in places).
The original goals of the book, from 10-years ago, have been met, i.e., discuss what is currently known about software engineering based on an analysis of all the publicly available software engineering data, and having the pdf+data+code freely available for download. The definition of “all the public data” started out as being “all”, but as larger and higher quality data was discovered the corresponding were ignored.
The intended audience has always been software developers and their managers. Some experience of building software systems is assumed.
How much data is there? The data directory contains 1,142 csv files and 985 R files, the book cites 895 papers that have data available of which 556 are cited in figure captions; there are 628 figures. I am currently quoting the figure of 600+ for the ‘amount of data’.
Things that might be learned from the analysis has been discussed in previous posts on the chapters: Human cognition, Cognitive capitalism, Ecosystems, Projects and Reliability.
The analysis of the available data is like a join-the-dots puzzle, except that the 600+ dots are not numbered, some of them are actually specs of dust, and many dots are likely to be missing. The future of software engineering research is joining the dots to build an understanding of the processes involved in building and maintaining software systems; work is also needed to replicate some of the dots to confirm that they are not specs of dust, and to discover missing dots.
Some missing dots are very important. For instance, there is almost no data on software use, but there can be lots of data on fault experiences. Without software usage data it is not possible to estimate whether the software is very reliable (i.e., few faults experienced per amount of use), or very unreliable (i.e., many faults experienced per amount of use).
The book treats the creation of software systems as an economically motivated cognitive activity occurring within one or more ecosystems. Algorithms are now commodities and are not discussed. The labour of the cognitariate is the means of production of software systems, and this is the focus of the discussion.
Existing books treat the creation of software as a craft activity, with developers applying the skills and know-how acquired through personal practical experience. The craft approach has survived because building software systems has been a sellers market, customers have paid what it takes because the potential benefits have been so much greater than the costs.
Is software development shifting from being a sellers market to a buyers market? In a competitive market for development work and staff, paying people to learn from mistakes that have already been made by many others is an unaffordable luxury; an engineering approach, derived from evidence, is a lot more cost-effective than craft development.
As always, if you know of any interesting software engineering data, please let me know.
The Weirdest people in the world
Western, Educated, Industrialized, Rich and Democratic: WEIRD people are the subject of Joseph Henrich’s latest book “The Weirdest People in the World: How the West Became Psychologically Peculiar and Particularly Prosperous”.
This book is in the mold of Jared Diamond’s Guns, Germs, and Steel: The Fates of Human Societies, but comes at the topic from a psychological/sociological angle.
This very readable book is essential reading for anyone wanting to understand how very different WEIRD people are, along with the societies they have created, compared to people and societies in the rest of the world today and the entire world up until around 500 years ago.
The analysis of WEIRD people/societies has three components: why we are different (I’m assuming that most of this blog’s readers are WEIRD), the important differences that are known about, and the cultural/societal consequences (the particularly prosperous in the subtitle is a big clue).
Henrich cites data to back up his theories.
Starting around 1,500 years ago the Catholic church started enforcing a ban on cousin marriage, which was an almost universal practice at the time and is still widely practiced in non-WEIRD societies. Over time the rules got stricter, until by the 11th century people were not allowed to marry anyone related out to their sixth cousin. The rules were not always strictly enforced, as Henrich documents, but the effect was to change the organization of society from being kin-based to being institution-based (in particular institutions such as the Church and state). Finding a wife/husband required people to interact with others outside their extended family.
Effects claimed, operating over centuries, of the shift from extended families to nuclear families are that people learned what Henrich calls “impersonal prosociality”, e.g., feeling comfortable dealing with strangers. People became more altruistic, the impartial rule of law spread (including democracy and human rights), plus other behaviors needed for the smooth running of large social units (such as towns, cities and countries).
The overall impact was that social units of WEIRD people could grow to include tens of thousands, even millions, or people, and successfully operate at this scale. Information about beneficial inventions could diffuse rapidly and people were free(ish) to try out new things (i.e., they were not held back by family customs), and operating in a society with free movement of people there were lots of efficiencies, e.g., companies were not obligated to hire family members, and could hire the best person they could find.
Consequently, the West got to take full advantage of scientific progress, invent and mass produce stuff. Outcompeting the non-WEIRD world.
The big ideas kind of hang together. Some of the details seem like a bit of a stretch, but I’m no expert.
My WEIRD story occurred about five years ago, when I was looking for a publisher for the book I was working on. One interested editor sent out an early draft for review. One of the chapters discusses human cognition, and I pointed out that it did not matter that most psychology experiments had been done using WEIRD subjects, because software developers were WEIRD (citing Henrich’s 2010 WEIRD paper). This discussion of WEIRD people was just too much for one of the reviewers, who sounded like he was foaming at the mouth when reviewing my draft (I also said a few things about academic researchers that upset him).
Benchmarking desktop PCs circa 1990
Before buying a computer customers want to be confident of choosing the best they can get for the money, and performance has often been a major consideration. Computer benchmark performance results were once widely discussed.
Knight’s analysis of early mainframe performance was widely cited for many years.
Performance on the Byte benchmarks was widely cited before Intel started spending billions on advertising, clock frequency has not always had the brand recognition it has today.
The Byte benchmark was originally designed for Intel x86 processors running Microsoft DOS; The benchmark was introduced in the June 1985 issue, and was written in the still relatively new C language (earlier microprocessor benchmarks were often written in BASIC, because early micros often came with a free BASIC interpreter), it was updated in the 1990s to be Windows based, and implemented for Unix.
Benchmarking computers using essentially the same cpu architecture and operating system removes many complications that have to be addressed when these differ. Before Wintel wiped them out, computers from different manufacturers (and often the same manufacturer) contained completely different cpu architectures, ran different operating systems, and compilers were usually created in-house by the manufacturer (or some university who got a large discount on their computer purchase).
The Fall 1990 issue of Byte contains tables of benchmark results from 1988-90. What can we learn from these results?
The most important takeaway from the tables is that those performing the benchmarks appreciated the importance of measuring hardware performance using the applications that customers are likely to be running on their computer, e.g., word processors, spreadsheets, databases, scientific calculations (computers were still sufficiently niche back then that scientific users were a non-trivial percentage of the market), and compiling (hackers were a large percentage of Byte’s readership).
The C benchmarks attempted to measure CPU, FPU (built-in hardware support for floating-point arrived with the 486 in April 1989, prior to that it was an add-on chip that required spending more money), Disk and Video (at the time support for color was becoming mainstream, but bundled hardware graphics support still tended to be minimal).
Running the application benchmarks takes a lot of time, plus the necessary software (which takes time to install from floppies, the distribution technology of the day). Running the C benchmarks is much quicker and simpler.
Ideally the C benchmarks are a reliable stand-in for the application benchmarks (meaning that only the C benchmarks need be run).
Let’s fit some regression models to the measurements of the 61 systems benchmarked, all supporting hardware floating-point (code+data). Surprisingly there is no mention of such an exercise being done by the Byte staff, even though one of the scientific benchmarks included regression fitting.
The following fitted equations explain around 90% of the variance of the data, i.e., they are good fits.
For wordprocessing, the CPU benchmark explains around twice as much as the Disk benchmark.
For spreadsheets, CPU and Disk contribute about the same.
Database is nearly all Disk.
Scientific/Engineering is FPU, plus interactions with other components.
Compiling is CPU, plus interactions with other components.
Byte’s benchmark reports were great eye candy, and readers probably took away a rough feel for the performance of various systems. Perhaps somebody at the time also fitted regression models to the data. The magazine contained plenty of adverts for software to do this.
Learning useful stuff from the Human cognition chapter of my book
What useful, practical things might professional software developers learn from the Human cognition chapter in my evidence-based software engineering book (an updated beta was release this week)?
Last week I checked the human cognition chapter; what useful things did I learn (combined with everything I learned during all the other weeks spent working on this chapter)?
I had spent a lot of time of learning about cognition when writing my C book; for this chapter I was catching up on what had happened in the last 10 years, which included: building executable models has become more popular, sample size has gotten larger (mostly thanks to Mechanical Turk), more researchers are making their data available on the web, and a few new theories (but mostly refinements of existing ideas).
Software is created by people, and it always seemed obvious to me that human cognition was a major topic in software engineering. But most researchers in computing departments joined the field because of their interest in maths, computers or software. The lack of interested in the human element means that the topic is rarely a research topic. There is a psychology of programming interest group, but most of those involved don’t appear to have read any psychology text books (I went to a couple of their annual workshops, and while writing the C book I was active on their mailing list for a few years).
What might readers learn from the chapter?
Visual processing: the rationale given for many code layout recommendations is plain daft; people need to learn something about how the brain processes images.
Models of reading. Existing readability claims are a joke (or bad marketing, take your pick). Researchers have been using eye trackers, since the 1960s, to figure out what actually happens when people read text, and various models have been built. Market researchers have been using eye trackers for decades to work out where best to place products on shelves, to maximise sales. In the last 10 years software researchers have started using eye trackers to study how people read code; next they need to learn about some of the existing models of how people read text. This chapter contains some handy discussion and references.
Learning and forgetting: it takes time to become proficient; going on a course is the start of the learning process, not the end.
One practical take away for readers of this chapter is being able to give good reasons how other people’s proposals, that are claimed to be based on how the brain operates, won’t work as claimed because that is not how the brain works. Actually, most of the time it is not possible to figure out whether something will work as advertised (this is why user interface testing is such a prolonged, and expensive, process), but the speaker with the most convincing techno-babble often wins the argument 🙂
Readers might have a completely different learning experience from reading the human cognition chapter. What useful things did you learn from the human cognition chapter?
Learning useful stuff from the Cognitive capitalism chapter of my book
What useful, practical things might professional software developers learn from the Cognitive capitalism chapter in my evidence-based software engineering book?
This week I checked the cognitive capitalism chapter; what useful things did I learn (combined with everything I learned during all the other weeks spent working on this chapter)?
Software systems are the product of cognitive capitalism (more commonly known as economics).
My experience is that most software developers don’t know anything about economics, so everything in this chapter is likely to be new to them. The chapter is more tutorial like than the other chapters.
Various investment models are discussed. The problem with these kinds of models is obtaining reliable data. But, hopefully the modelling ideas will prove useful.
Things I learned about when writing the chapter include: social learning, group learning, and Open source licensing is a mess.
Building software systems usually requires that many of the individuals involved to do lots of learning. How do people decide what to learn, e.g., copy others or strike out on their own? This problem is not software specific, in fact social learning appears to be one of the major cognitive abilities that separates us from other apes.
Organizational learning and forgetting is much talked about, and it was good to find some data dealing with this. Probably not applicable to most people.
Open source licensing is a mess in that software containing a variety of, possible incompatible, licenses often gets mixed together. What future lawsuits await?
For me, potentially the most immediately useful material was group learning; there are some interesting models for how this sometimes works.
Readers might have a completely different learning experience from reading the cognitive capitalism chapter. What useful things did you learn from the cognitive capitalism chapter?
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