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My 2024 in software engineering
Readers are unlikely to have noticed something that has not been happening during the last few years. The plot below shows, by year of publication, the number of papers cited (green) and datasets used (red) in my 2020 book Evidence-Based Software Engineering. The fitted red regression lines suggest that the 20s were going to be a period of abundant software engineering data; this has not (yet?) happened (the blue line is a local regression fit, i.e., loess). In 2020 COVID struck, and towards the end of 2022 Large Language Models appeared and sucked up all the attention in the software research ecosystem, and there is lots of funding; data gathering now looks worse than boring (code+data):
LLMs are showing great potential as research tools, but researchers are still playing with them in the sandpit.
How many AI startups are there in London? I thought maybe one/two hundred. A recruiter specializing in AI staffing told me that he would estimate around four hundred; this was around the middle of the year.
What did I learn/discover about software engineering this year?
Regular readers may have noticed a more than usual number of posts discussing papers/reports from the 1960s, 1970s and early 1980s. There is a night and day difference between software engineering papers from this start-up period and post mid-1980s papers. The start-up period papers address industry problems using sophisticated mathematical techniques, while post mid-1980s papers pay lip service to industrial interests, decorating papers with marketing speak, such as maintainability, readability, etc. Mathematical orgasms via the study of algorithms could be said to be the focus of post mid-1980s researchers. So-called software engineering departments ought to be renamed as Algorithms department.
Greg Wilson thinks that the shift happened in the 1980s because this was the decade during which the first generation of ‘trained in software’ people (i.e., emphasis on mathematics and abstract ideas) became influential academics. Prior generations had received a practical training in physics/engineering, and been taught the skills and problem-solving skills that those disciplines had refined over centuries.
My research is a continuation of the search for answers to the same industrial problems addressed by the start-up researchers.
In the second half of the year I discovered the mathematical abilities of LLMs, and started using them to work through the equations for various models I had in mind. Sometimes the final model turned out to be trivial, but at least going through the process cleared away the complications in my mind. According to reports, OpenAI’s next, as yet unreleased, model has super-power maths abilities. It will still need a human to specify the equations to solve, so I am not expecting to have nothing to blog about.
Analysis/data in the following blog posts, from the last 12-months, belongs in my book Evidence-Based Software Engineering, in some form or other:
Small business programs: A dataset in the research void
Putnam’s software equation debunked (the book is non-committal).
if statement conditions, some basic measurements
Number of statement sequences possible using N if-statements; perhaps.
A new NASA software dataset from the 1970s
A surprising retrospective task estimation dataset
Average lines added/deleted by commits across languages
Census of general purpose computers installed in the 1960s
Some information on story point estimates for 16 projects
Agile and Waterfall as community norms
Median system cpu clock frequency over last 15 years
The evidence-based software engineering Discord channel continues to tick over (invitation), with sporadic interesting exchanges.
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