The Shape of Code

Tens of millions of people get paid to work on the creation and maintenance of software systems, by companies employing thousands of developers to those employing a single developer (in the UK there are almost 300K registered software companies; 5% of registered companies).

This huge ecosystem is almost completely ignored by the software engineering research community. Academics in computing/software are more interested in technical issue, and industry is an ecosystem they rarely interact with (some claim that student employment keeps them in contact with industry).

There are researchers in business and economics departments who study employment, e.g., careers, organization of workers and companies. The scientific study of work started at the beginning of the 1900s, originally focused on the manufacturing and included office work as that grew to employ a significant percentage of the workforce. Until recently, the percentage of the workforce employed to create/maintain software was not large enough to attract the attention of these researchers, and even now it’s often lumped together with other jobs that mostly involve some form of intellectual activity.

Employee related issues of interest to those involved in managing work on software systems are heavily influenced by the characteristics of the business ecosystem in which they work. The software driven business ecosystems are continually changing, with companies growing, merging and going bust as new markets emerge, grow, saturate, and sometime disappear. This constant change creates employment uncertainty, and lots of opportunities for competent people (creating a staff retention problem). For more stable industries, it’s possible for researchers to model employee start/promotion/leaving transitions using Markov models (example of ChatGPT 1o-preview solving a recurrence model of the staffing relationships in a 3-level employment hierarchy). The book “Stochastic Models for Social Processes” by D. J. Bartholomew gives a practical introduction to the use of Markov models for this kind of analysis.

The evolution and constant introduction of new technologies can make it difficult to find people with the appropriate skills. Companies may tune the wording of job adverts to give the impress of using ‘modern’ technologies, or post fake job adverts (to increase their attractiveness and suggest a feeling of growth), and people tune their CV to appeal to employers (some out right lie about their skills; many managers have told me that around 90% of applicants don’t have the primary skill sought by the employer). Well paid jobs can attract lots of applicants, filtering/interviewing can be an expensive process (not least because the same job title can denote different seniority in different companies). Matching CVs to job requirements sounds like the perfect use case for LLMs. I suspect that LLM tuning of CVs/adverts will just increase costs/uncertainty.

The constant churn of technologies forces employees to make decisions about whether to happily spend many years being well paid to become an expert in a niche with decreasing industry demand, or to invest in starting again as a non-expert doing something new (and initially less well paid).

What is the best to organize engineering employees at a company-wide scale? Matrix management was once the standard answer, but these days, scaled agile is a fashionable answer. An evidence-based answer will have to wait until the lawyers in a large organization allow somebody with the necessary skills access to the appropriate data.

With the contents of job sites being scraped, along with LinkedIn, I’m optimistic that some meaningful employment data will slowly become available. Will the analysis of this data uncover patterns of practical use (other than interesting blog posts) to employers/employees? We will have to wait and see.