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Tracking software evolution via its Changelog

Derek Jones from The Shape of Code

Software that is used evolves. How fast does software evolve, e.g., much new functionality is added and how much existing functionality is updated?

A new software release is often accompanied by a changelog which lists new, changed and deleted functionality. When software is developed using a continuous release process, the changelog can be very fine-grained.

The changelog for the Beeminder app contains 3,829 entries, almost one per day since February 2011 (around 180 entries are not present in the log I downloaded, whose last entry is numbered 4012).

Is it possible to use the information contained in the Beeminder changelog to estimate the rate of growth of functionality of Beeminder over time?

My thinking is driven by patterns in a plot of the Renzo Pomodoro dataset. Renzo assigned a tag-name (sometimes two) to each task, which classified the work involved, e.g., @planning. The following plot shows the date of use of each tag-name, over time (ordered vertically by first use). The first and third black lines are fitted regression models of the form 1-e^{-K*days}, where: K is a constant and days is the number of days since the start of the interval fitted; the second (middle) black line is a fitted straight line.

at-words usage, by date.

How might a changelog line describing a day’s change be distilled to a much shorter description (effectively a tag-name), with very similar changes mapping to the same description?

Named-entity recognition seemed like a good place to start my search, and my natural language text processing tool of choice is still spaCy (which continues to get better and better).

spaCy is Python based and the processing pipeline could have all been written in Python. However, I’m much more fluent in awk for data processing, and R for plotting, so Python was just used for the language processing.

The following shows some Beeminder changelog lines after stripping out urls and formatting characters:

Cheapo bug fix for erroneous quoting of number of safety buffer days for weight loss graphs.
Bugfix: Response emails were accidentally off the past couple days; fixed now. Thanks to user bmndr.com/laur  for alerting us!  
More useful subject lines in the response emails, like "wrong lane!" or whatnot.
Clearer/conciser stats at bottom of graph pages. (Will take effect when you enter your next datapoint.) Progress, rate, lane, delta.  
Better handling of significant digits when displaying numbers. Cf stackoverflow.com/q/5208663

The code to extract and print the named-entities in each changelog line could not be simpler.

import spacy
import sys

nlp = spacy.load("en_core_web_sm") # load trained English pipelines

count=0 
        
for line in sys.stdin:
   count += 1 
   print(f'> {count}: {line}')
#
   doc=nlp(line) # do the heavy lifting
#          
   for ent in doc.ents:  # iterate over detected named-entities
      print(ent.lemma_, ent.label_)

To maximize the similarity between named-entities appearing on different lines the lemmas are printed, rather than original text (i.e., words appear in their base form).

The label_ specifies the kind of named-entity, e.g., person, organization, location, etc.

This code produced 2,225 unique named-entities (5,302 in total) from the Beeminder changelog (around 0.6 per day), and failed to return a named-entity for 33% of lines. I was somewhat optimistically hoping for a few hundred unique named-entities.

There are several problems with this simple implementation:

  • each line is considered in isolation,
  • the change log sometimes contains different names for the same entity, e.g., a person’s full name, Christian name, or twitter name,
  • what appear to be uninteresting named-entities, e.g., numbers and dates,
  • the language does not know much about software, having been training on a corpus of general English.

Handling multiple names for the same entity would a lot of work (i.e., I did nothing), ‘uninteresting’ named-entities can be handled by post-processing the output.

A language processing pipeline that is not software-concept aware is of limited value. spaCy supports adding new training models, all I need is a named-entity model trained on manually annotated software engineering text.

The only decent NER training data I could find (trained on StackOverflow) was for BERT (another language processing tool), and the data format is very different. Existing add-on spaCy models included fashion, food and drugs, but no software engineering.

Time to roll up my sleeves and create a software engineering model. Luckily, I found a webpage that provided a good user interface to tagging sentences and generated the json file used for training. I was patient enough to tag 200 lines with what I considered to be software specific named-entities. … and now I have broken the NER model I built…

The following plot shows the growth in the total number of named-entities appearing in the changelog, and the number of unique named-entities (with the 1,996 numbers and dates removed; code+data);

Growth of total and unique named-entities in the Beeminder changelog.

The regression fits (red lines) are quadratics, slightly curving up (total) and down (unique); the linear growth components are: 0.6 per release for total, and 0.46 for unique.

Including software named-entities is likely to increase the total by at least 15%, but would have little impact on the number of unique entries.

This extraction pipeline processes one release line at a time. Building a set of Beeminder tag-names requires analysing the changelog as a whole, which would take a lot longer than the day spent on this analysis.

The Beeminder developers have consistently added new named-entities to the changelog over more than eleven years, but does this mean that more features have been consistently added to the software (or are they just inventing different names for similar functionality)?

It is not possible to answer this question without access to the code, or experience of using the product over these eleven years.

However, staying in business for eleven years is a good indicator that the developers are doing something right.

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The software heritage of K&R C

Derek Jones from The Shape of Code

The mission statement of the Software Heritage is “… to collect, preserve, and share all software that is publicly available in source code form.”

What are the uses of the preserved source code that is collected? Lots of people visit preserved buildings, but very few people are interested in looking at source code.

One use-case is tracking the evolution of changes in developer usage of various programming language constructs. It is possible to use Github to track the adoption of language features introduced after 2008, when the company was founded, e.g., new language constructs in Java. Over longer time-scales, the Software Heritage, which has source code going back to the 1960s, is the only option.

One question that keeps cropping up when discussing the C Standard, is whether K&R C continues to be used. Technically, K&R C is the language defined by the book that introduced C to the world. Over time, differences between K&R C and the C Standard have fallen away, as compilers cease supporting particular K&R ways of doing things (as an option or otherwise).

These days, saying that code uses K&R C is taken to mean that it contains functions defined using the K&R style (see sentence 1818), e.g.,

writing:

int f(a, b)
int a;
float b;
{
/* declarations and statements */
}

rather than:

int f(int a, float b)
{
/* declarations and statements */
}

As well as the syntactic differences, there are semantic differences between the two styles of function definition, but these are not relevant here.

How much longer should the C Standard continue to support the K&R style of function definition?

The WG14 committee prides itself on not breaking existing code, or at least not lots of it. How much code is out there, being actively maintained, and containing K&R function definitions?

Members of the committee agree that they rarely encounter this K&R usage, and it would be useful to have some idea of the decline in use over time (with the intent of removing support in some future revision of the standard).

One way to estimate the evolution in the use/non-use of K&R style function definitions is to analyse the C source created in each year since the late 1970s.

The question is then: How representative is the Software Heritage C source, compared to all the C source currently being actively maintained?

The Software Heritage preserves publicly available source, plus the non-public, proprietary source forming the totality of the C currently being maintained. Does the public and non-public C source have similar characteristics, or are there application domains which are poorly represented in the publicly available source?

Embedded systems is a very large and broad application domain that is poorly represented in the publicly available C source. Embedded source tends to be heavily tied to the hardware on which it runs, and vendors tend to be paranoid about releasing internal details about their products.

The various embedded systems domains (e.g., 8, 16, 32, 64-bit processor) tend to be a world unto themselves, and I would not be surprised to find out that there are enclaves of K&R usage (perhaps because there is no pressure to change, or because the available tools are ancient).

At the moment, the Software Heritage don’t offer code search functionality. But then, the next opportunity for major changes to the C Standard is probably 5-years away (the deadline for new proposals on the current revision has passed); plenty of time to get to a position where usage data can be obtained 🙂

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Production of software may continue to be craft based

Derek Jones from The Shape of Code

Andrew Carnegie made his fortune in the steel industry, and his autobiography is a fascinating insight into the scientific vs. craft/folklore approach to smelting iron ore. Carnegie measured the processes involved in smelting; he tracked the input and outputs involved in the smelting process, and applied the newly available scientific knowledge (e.g., chemistry) to minimize the resources needed to extract iron from ore. Other companies continued to treat Iron smelting as a suck-it-and-see activity, driven by personal opinion and the application of techniques that had worked in the past.

The technique of using what-worked-last-time can be a successful strategy when the variability of the inputs is low. In the case of smelting Iron there was a lot of variability in the Iron ore, Limestone and Coke fed into the furnaces. The smelting companies in Carnegie’s day ‘solved’ this input variability problem by restricting their purchase of raw materials to mines that delivered material that worked last time.

Hiring an experienced chemist (the only smelting company to do so), Carnegie found out that the quality of ore (i.e., percentage Iron content) in some mines with a high reputation was much lower than the ore quality of some mines with a low reputation; Carnegie was able to obtain a low price for high quality ore because other companies did not appreciate its characteristics (and shunned using it). Other companies were unable to extract Iron from high quality ore because they stuck to using a process that worked for lower quality ore (the amount of Limestone and Coke added to the smelting process has to be adjusted based on the Iron content of the ore, otherwise the process may deliver poor results, or even fail to produce Iron; see chapter 13).

When Carnegie’s application of scientific knowledge, and his competitors’ opinion driven production, is combined with being a good businessman, it’s no surprise that Carnegie made a fortune from his Iron smelting business.

What are the parallels between iron smelting in Carnegie’s day and the software industry?

An obvious parallel is the industry dominance of opinion driven processes. But then, the lack of any scientific basis for the processes involved in building software systems would seem to make drawing parallels a pointless exercise.

Let’s assume that there was a scientific basis for some of the major processes involved in software engineering. Would any of these science-based processes be adopted?

The reason for using science based knowledge and mechanization is to reduce costs, which may lead to increased profits or just staying in business (in a Red Queen’s race).

Agriculture is an example of a business where science and mechanization dominate, and building construction is a domain where this has not happened. Perhaps building construction will become more mechanized when unknown missing components become available (mechanization was available for agricultural processes in the 1700s, but they did not spread for a century or two, e.g., threshing machines).

It’s possible to find parallels between software engineering and the smelting process, agriculture, and building construction. In fact, it parallels can probably be found between software engineering and any other major business domain.

Drawing parallels between software engineering and other major business domains creates a sense of familiarity. In practice, software is unlike most existing business domains in that software products are one-off creations of an intangible good, which has (virtually) zero cost of reproduction, while the economics of creating tangible goods (e.g., by smelting, sowing and reaping, or building houses) is all about reducing the far from zero cost of reproduction.

Perhaps the main take-away from the history of the production of tangible goods is that the scientific method has not always supplanted the craft approach to production.

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Widely used programming languages: past, present, and future

Derek Jones from The Shape of Code

Programming languages are like pop groups in that they have followers, fans and supporters; new ones are constantly being created and some eventually become widely popular, while those that were once popular slowly fade away or mutate into something else.

Creating a language is a relatively popular activity. Science fiction and fantasy authors have been doing it since before computers existed, e.g., the Elf language Quenya devised by Tolkien, and in the computer age Star Trek’s Klingon. Some very good how-to books have been written on the subject.

As soon as computers became available, people started inventing programming languages.

What have been the major factors influencing the growth to widespread use of a new programming languages (I’m ignoring languages that become widespread within application niches)?

Cobol and Fortran became widely used because there was widespread implementation support for them across computer manufacturers, and they did not have to compete with any existing widely used languages. Various niches had one or more languages that were widely used in that niche, e.g., Algol 60 in academia.

To become widely used during the mainframe/minicomputer age, a new language first had to be ported to the major computers of the day, whose products sometimes supported multiple, incompatible operating systems. No new languages became widely used, in the sense of across computer vendors. Some new languages were widely used by developers, because they were available on IBM computers; for several decades a large percentage of developers used IBM computers. Based on job adverts, RPG was widely used, but PL/1 not so. The use of RPG declined with the decline of IBM.

The introduction of microcomputers (originally 8-bit, then 16, then 32, and finally 64-bit) opened up an opportunity for new languages to become widely used in that niche (which would eventually grow to be the primary computing platform of its day). This opportunity occurred because compiler vendors for the major languages of the day did not want to cannibalize their existing market (i.e., selling compilers for a lot more than the price of a microcomputer) by selling a much lower priced product on microcomputers.

BASIC became available on practically all microcomputers, or rather some dialect of BASIC that was incompatible with all the other dialects. The availability of BASIC on a vendor’s computer promoted sales of the hardware, and it was not worthwhile for the major vendors to create a version of BASIC that reduced portability costs; the profit was in games.

The dominance of the Microsoft/Intel partnership removed the high cost of porting to lots of platforms (by driving them out of business), but created a major new obstacle to the wide adoption of new languages: Developer choice. There had always been lots of new languages floating around, but people only got to see the subset that were available on the particular hardware they targeted. Once the cpu/OS (essentially) became a monoculture most new languages had to compete for developer attention in one ecosystem.

Pascal was in widespread use for a few years on micros (in the form of Turbo Pascal) and university computers (the source of Wirth’s ETH compiler was freely available for porting), but eventually C won developer mindshare and became the most widely used language. In the early 1990s C++ compiler sales took off, but many developers were writing C with a few C++ constructs scattered about the code (e.g., use of new, rather than malloc/free).

Next, the Internet took off, and opened up an opportunity for new languages to become dominant. This opportunity occurred because Internet related software was being made freely available, and established compiler vendors were not interested in making their products freely available.

There were people willing to invest in creating a good-enough implementation of the language they had invented, and giving it away for free. Luck, plus being in the right place at the right time resulted in PHP and Javascript becoming widely used. Network effects prevent any other language becoming widely used. Compatible dialects of PHP and Javascript may migrate widespread usage to quite different languages over time, e.g., Facebook’s Hack.

Java rode to popularity on the coat-tails of the Internet, and when it looked like security issues would reduce it to niche status, it became the vendor supported language for one of the major smart-phone OSs.

Next, smart-phones took off, but the availability of Open Source compilers closed the opportunity window for new languages to become dominant through lack of interest from existing compiler vendors. Smart-phone vendors wanted to quickly attract developers, which meant throwing their weight behind a language that many developers were already familiar with; Apple went with Objective-C (which evolved to Swift), Google with Java (which evolved to Kotlin, because of the Oracle lawsuit).

Where does Python fit in this grand scheme? I don’t yet have an answer, or is my world-view wrong to treat Python usage as being as widespread as C/C++/Java?

New programming languages continue to be implemented; I don’t see this ever stopping. Most don’t attract more users than their implementer, but a few become fashionable amongst the young, who are always looking to attach themselves to something new and shiny.

Will a new programming language ever again become widely used?

Like human languages, programming languages experience strong networking effects. Widely used languages continue to be widely used because many companies depend on code written in it, and many developers who can use it can obtain jobs; what company wants to risk using a new language only to find they cannot hire staff who know it, and there are not many people willing to invest in becoming fluent in a language with no immediate job prospects.

Today’s widely used programmings languages succeeded in a niche that eventually grew larger than all the other computing ecosystems. The Internet and smart-phones are used by everybody on the planet, there are no bigger ecosystems to provide new languages with a possible route to widespread use. To be widely used a language first has to become fashionable, but from now on, new programming languages that don’t evolve from (i.e., be compatible with) current widely used languages are very unlikely to migrate from fashionable to widely used.

It has always been possible for a proficient developer to dedicate a year+ of effort to create a new language implementation. Adding the polish need to make it production ready used to take much longer, but these days tool chains such as LLVM supply a lot of the heavy lifting. The problem for almost all language creators/implementers is community building; they are terrible at dealing with other developers.

It’s no surprise that nearly all the new languages that become fashionable originate with language creators who work for a company that happens to feel a need for a new language. Examples include:

  • Go created by Google for internal use, and attracted an outside fan base. Company languages are not new, with IBM’s PL/1 being the poster child (or is there a more modern poster child). At the moment Go is a trendy language, and this feeds a supply of young developers willing to invest in learning it. Once the trendiness wears off, Google will start to have problems recruiting developers, the reason: Being labelled as a Go developer limits job prospects when few other companies use the language. Talk to a manager who has tried to recruit developers to work on applications written in Fortran, Pascal and other once-widely used languages (and even wannabe widely used languages, such as Ada),
  • Rust a vanity project from Mozilla, which they have now abandoned. Did Rust become fashionable because it arrived at the right time to become the not-Google language? I await a PhD thesis on the topic of the rise and fall of Rust,
  • Microsoft’s C# ceased being trendy some years ago. These days I don’t have much contact with developers working in the Microsoft ecosystem, so I don’t know anything about the state of the C# job market.

Every now and again a language creator has the social skills needed to start an active community. Zig caught my attention when I read that its creator, Andrew Kelley, had quit his job to work full-time on Zig. Two and a-half years later Zig has its own track at FOSEM’21.

Will Zig become the next fashionable language, as Rust/Go popularity fades? I’m rooting for Zig because of its name, there are relatively few languages whose name starts with Z; the start of the alphabet is over-represented with language names. It would be foolish to root for a language because of a belief that it has magical properties (e.g., powerful, readable, maintainable), but the young are foolish.

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Growth in number of packages for widely used languages

Derek Jones from The Shape of Code

These days a language’s ecosystem of add-ons, such as packages, is often more important than the features provided by the language (which usually only vary in their syntactic sugar, and built-in support for some subset of commonly occurring features).

Use of a particular language grows and shrinks, sometimes over very many decades. Estimating the number of users of a language is difficult, but a possible proxy is ecosystem activity in the form of package growth/decline. However, it will take many several decades for the data needed to test how effective this proxy might be.

Where are we today?

The Module Counts website is the home for a project that counts the number of libraries/packages/modules contained in 26 language specific repositories. Daily data, in some cases going back to 2010, is available as a csv :-) The following are the most interesting items I discovered during a fishing expedition.

The csv file contains totals, and some values are missing (which means specifying an ‘ignore missing values’ argument to some functions). Some repos have been experiencing large average daily growth (e.g., 65 for PyPI, and 112 for Maven Central-Java), while others are more subdued (e.g., 0.7 for PERL and 3.9 for R’s CRAN). Apart from a few days, the daily change is positive.

Is the difference in the order of magnitude growth due to number of active users, number of packages that currently exist, a wide/narrow application domain (Python is wide, while R’s is narrow), the ease of getting a package accepted, or something else?

The plots below show how PyPI has been experiencing exponential growth of a kind (the regression model fitted to the daily total has the form e^{1.01days+days^2}, where days is the number of days since 2010-01-01; the red line is the daily diff of this equation), while Ruby has been experiencing a linear decline since late 2014 (all code+data):

Daily change in the number of packages in PyPI and Rubygems.

Will the five-year decline in new submissions to Rubygems continue, and does this point to an eventual demise of Ruby (a few decades from now)? Rubygems has years to go before it reaches PERL’s low growth rate (I think PERL is in terminal decline).

Are there any short term patterns, say at the weekly level? Autocorrelation is a technique for estimating the extent to which today’s value is affected by values from the immediate past (usually one or two measurement periods back, i.e., yesterday or the day before that). The two plots below show the autocorrelation for daily changes, with lag in days:

Autocorrelation of daily changes in PyPI and Maven-Java package counts.

The recurring 7-day ‘peaks’ show the impact of weekends (I assume). Is the larger ”weekend-effect’ for Java, compared to PyPI, due to Java usage including a greater percentage of commercial developers (who tend not to work at the weekend)?

I did not manage to find any seasonal effect, e.g., more submissions during the winter than the summer. But I only checked a few of the languages, and only for a single peak (see code for details).

Another way of tracking package evolution is version numbering. For instance, how often do version numbers change, and which component, e.g., major/minor. There have been a couple of studies looking at particular repos over a few years, but nobody is yet recording broad coverage daily, over the long term 😉

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Learning useful stuff from the Ecosystems chapter of my book

Derek Jones from The Shape of Code

What useful, practical things might professional software developers learn from the Ecosystems chapter in my evidence-based software engineering book?

This week I checked the ecosystems chapter; what useful things did I learn (combined with everything I learned during all the other weeks spent working on this chapter)?

A casual reader would conclude that software engineering ecosystems involved lots of topics, with little or no theory connecting them. I had great plans for the connecting theories, but lack of detailed data, time and inspiration means the plans remain in my head (e.g., modelling the interaction between the growth of source code written in a particular language and the number of developers actively using that language).

For managers, the usefulness of this chapter is the strategic perspective it provides. How does what they and others are doing relate to everything else, and what patterns of evolution are to be expected?

Software people like to think that everything about software is unique. Software is unique, but the activities around it follow patterns that have been followed by other unique technologies, e.g., the automobile and jet engines. There is useful stuff to be learned from non-software ecosystems, and the chapter discusses some similarities I have learned about.

There is lots more evidence of the finite lifetime of software related items: lifetime of products, Linux distributions, packages, APIs and software careers.

Some readers might be surprised by the amount of discussion about what is now historical hardware. Software needs hardware to execute it, and the characteristics of the hardware of the day can have a significant impact on the characteristics of the software that gets written. I suspect that most of this discussion will not be that useful to most readers, but it provides some context around why things are the way they are today.

Readers with a wide knowledge of software ecosystems will notice that several major ecosystems barely get a mention. Embedded systems is a huge market, as is Microsoft Windows, and very many professional developers use C++. However, to date the focus of most research has been around Linux and Android (because its use of Java, a language often taught in academia), and languages that have a major package repository. So the ecosystems chapter presents a rather blinkered view of software engineering ecosystems.

What did I learn from this chapter?

Software ecosystems are bigger and more complicated that I had originally thought.

Readers might have a completely different learning experience from reading the ecosystems chapter. What useful things did you learn from the ecosystems chapter?

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Happy 60th birthday: Algol 60

Derek Jones from The Shape of Code

Report on the Algorithmic Language ALGOL 60 is the title of a 16-page paper appearing in the May 1960 issue of the Communication of the ACM. Probably one of the most influential programming languages, and a language that readers may never have heard of.

During the 1960s there were three well known, widely used, programming languages: Algol 60, Cobol, and Fortran.

When somebody created a new programming languages Algol 60 tended to be their role-model. A few of the authors of the Algol 60 report cited beauty as one of their aims, a romantic notion that captured some users imaginations. Also, the language was full of quirky, out-there, features; plenty of scope for pin-head discussions.

Cobol appears visually clunky, is used by business people and focuses on data formatting (a deadly dull, but very important issue).

Fortran spent 20 years catching up with features supported by Algol 60.

Cobol and Fortran are still with us because they never had any serious competition within their target markets.

Algol 60 had lots of competition and its successor language, Algol 68, was groundbreaking within its academic niche, i.e., not in a developer useful way.

Language family trees ought to have Algol 60 at, or close to their root. But the Algol 60 descendants have been so successful, that the creators of these family trees have rarely heard of it.

In the US the ‘military’ language was Jovial, and in the UK it was Coral 66, both derived from Algol 60 (Coral 66 was the first language I used in industry after graduating). I used to hear people saying that Jovial was derived from Fortran; another example of people citing the language the popular language know.

Algol compiler implementers documented their techniques (probably because they were often academics); ALGOL 60 Implementation is a real gem of a book, and still worth a read today (as an introduction to compiling).

Algol 60 was ahead of its time in supporting undefined behaviors 😉 Such as: “The effect, of a go to statement, outside a for statement, which refers to a label within the for statement, is undefined.”

One feature of Algol 60 rarely adopted by other languages is its parameter passing mechanism, call-by-name (now that lambda expressions are starting to appear in widely used languages, call-by-name has a kind-of comeback). Call-by-name essentially has the same effect as textual substitution. Given the following procedure (it’s not a function because it does not return a value):

procedure swap (a, b);
   integer a, b, temp;
begin
temp := a;
a := b;
b:= temp
end;

the effect of the call: swap(i, x[i]) is:

  temp := i;
  i := x[i];
  x[i] := temp

which might come as a surprise to some.

Needless to say, programmers came up with ‘clever’ ways of exploiting this behavior; the most famous being Jensen’s device.

The follow example of go to usage appears in: International Standard 1538 Programming languages – ALGOL 60 (the first and only edition appeared in 1984, after most people had stopped using the language):

go to if Ab < c then L17
  else g[if w < 0 then 2 else n]

Orthogonality of language use won out over the goto FUD.

The Software Preservation Group is a great resource for Algol 60 books and papers.

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Natural elimination, or the survival of the good enough

Derek Jones from The Shape of Code

Thanks to Darwin, the world is full of people who think that evolution, in nature, works by: natural selection, or the survival of the fittest. I thought this until I read “Good Enough: The Tolerance for Mediocrity in Nature and Society” by Daniel Milo.

Milo makes a very convincing case that nature actually works by: natural elimination, or the survival of the good enough.

Why might Darwin have gone with natural selection in his book, On the Origin of Species? Milo makes the point that the only real evidence that Darwin had to work with was artificial selection, that is the breeding of farm animals and domestic pets to select for traits that humans found desirable. Darwin’s visit to the Galápagos islands triggered a way of thinking, it did not provide him with the evidence he needed; Darwin’s Finches have become a commonly cited example of natural selection at work, but while Darwin made the observations it was not until 80 years later that somebody else spotted their relevance.

The Origin of Species, or to use its full title: “On the Origin of Species by means of natural selection, or the preservation of favored races in the struggle for life.” is full of examples and terminology relating to artificial selection.

Natural selection, or natural elimination, isn’t the result the same?

Natural selection implies an optimization process, e.g., breeders selecting for a strain of cows that produce the most milk.

Natural elimination is a good enough process, i.e., a creature needs a collection of traits that are good enough for them to create the next generation.

A long-standing problem with natural selection is that it fails to explain the diversity present in a natural population of some breed of animal (there is very little diversity in each breed of farm animal, they have been optimized for consistency). Diversity is not a problem for natural elimination, which does not reduce differences in its search for fitness.

The diversity produced as a consequence of natural elimination creates a population containing many neutral traits (i.e., characteristics that have no positive or negative impact on continuing survival). When a significant change in the environment occurs, one or more of the neutral traits may suddenly have positive or negative survival consequences; the creatures with the positive traits have opportunity time to adapt to the changed environment. A population whose members possess a diverse range of neutral traits has a higher chance of long-term survival than a population where diversity has been squeezed in the quest for the fittest.

I think that natural elimination also applies within software ecosystems. Commercial products survive if enough customers buy them, software developers need good enough know-how to get the job done.

I’m sure customers would prefer software ecosystems to operate on the principle of survival of the fittest (it reduces their costs). Over the long term is society best served by diverse software ecosystems or softwaremonocultures? Diversity is a way of encouraging competition, but over time there is diminishing returns on the improvements.

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Growth and survival of gcc options and processor support

Derek Jones from The Shape of Code

Like any actively maintained software, compilers get more complicated over time. Languages are still evolving, and options are added to control the support for features. New code optimizations are added, which don’t always work perfectly, and options are added to enable/disable them. New ways of combining object code and libraries are invented, and new options are added to allow the desired combination to be selected.

The web pages summarizing the options for gcc, for the 96 versions between 2.95.2 and 9.1 have a consistent format; which means they are easily scrapped. The following plot shows the number of options relating to various components of the compiler, for these versions (code+data):

Number of options supported by various components of gcc, over 20 years.

The total number of options grew from 632 to 2,477. The number of new optimizations, or at least the options supporting them, appears to be leveling off, but the number of new warnings continues to increase (ah, the good ol’ days, when -Wall covered everything).

The last phase of a compiler is code generation, and production compilers are generally structured to enable new processors to be supported by plugging in an appropriate code generator; since version 2.95.2, gcc has supported 80 code generators.

What can be added can be removed. The plot below shows the survival curve of gcc support for processors (80 supported cpus, with support for 20 removed up to release 9.1), and non-processor specific options (there have been 1,091 such options, with 214 removed up to release 9.1); the dotted lines are 95% confidence internals.

Survival curve of gcc options and support for specific processors, over 20 years.

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Lehman ‘laws’ of software evolution

Derek Jones from The Shape of Code

The so called Lehman laws of software evolution originated in a 1968 study, and evolved during the 1970s; the book “Program Evolution: processes of software change” by Lehman and Belady was published in 1985.

The original work was based on measurements of OS/360, IBM’s flagship operating system for the computer industries flagship computer. IBM dominated the computer industry from the 1950s, through to the early 1980s; OS/360 was the Microsoft Windows, Android, and iOS of its day (in fact, it had more developer mind share than any of these operating systems).

In its day, the Lehman dataset not only bathed in reflected OS/360 developer mind-share, it was the only public dataset of its kind. But today, this dataset wouldn’t get a second look. Why? Because it contains just 19 measurement points, specifying: release date, number of modules, fraction of modules changed since the last release, number of statements, and number of components (I’m guessing these are high level programs or interfaces). Some of the OS/360 data is plotted in graphs appearing in early papers, and can be extracted; some of the graphs contain 18, rather than 19, points, and some of the values are not consistent between plots (extracted data); in later papers Lehman does point out that no statistical analysis of the data appears in his work (the purpose of the plots appears to be decorative, some papers don’t contain them).

One of Lehman’s early papers says that “… conclusions are based, comes from systems ranging in age from 3 to 10 years and having been made available to users in from ten to over fifty releases.“, but no other details are given. A 1997 paper lists module sizes for 21 releases of a financial transaction system.

Lehman’s ‘laws’ started out as a handful of observations about one very large software development project. Over time ‘laws’ have been added, deleted and modified; the Wikipedia page lists the ‘laws’ from the 1997 paper, Lehman retired from research in 2002.

The Lehman ‘laws’ of software evolution are still widely cited by academic researchers, almost 50-years later. Why is this? The two main reasons are: the ‘laws’ are sufficiently vague that it’s difficult to prove them wrong, and Lehman made a large investment in marketing these ‘laws (e.g., publishing lots of papers discussing these ‘laws’, and supervising PhD students who researched software evolution).

The Lehman ‘laws’ are not useful, in the sense that they cannot be used to make predictions; they apply to large systems that grow steadily (i.e., the kind of systems originally studied), and so don’t apply to some systems, that are completely rewritten. These ‘laws’ are really an indication that software engineering research has been in a state of limbo for many decades.