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Student projects for 2024/2025
It will soon be that time of year when university students are looking for an interesting idea for a project. On an irregular basis, I post some ideas for thesis projects (here and here); primarily for students studying computing. In a change of direction, this post suggests software related ideas for business student projects.
Two idea areas require data analysis skills, one requires people skills, and one an interest in theory.
More suggestions welcome in the comments.
Career paths in software
Organizations employ people to work on software systems. What is the career path of people who work on software systems? Question include: how long people stay in a particular job or company, and salary changes over time (the only data I know of investigates the career paths of 500 people working in IT).
Governments are interested in employment, and they collect and publish data at various levels of granularity. The US Bureau of Labor Statistics contains a vast amount of information, but finding the bits of interest can require a lot of work.
In the US, government employee salary is public information, and various sites make this available, e.g., OpePayrolls and Transparent California. There is a Japanese Open Salaries, and various commercial companies operate an open salary policy (Buffer is perhaps the most famous).
This project requires students with some data analysis skills.
There is some data on job postings,
Computer company lifecycle
Companies are born, do business and eventually die (unless bought/merged). How do the lifecycle characteristics of computer companies differ from companies doing business in other domains? Lifecycle characteristics of interest might include profiles of age, number of employees, and profitability. What are the consequences, if any, of these differences?
Details of all UK registered companies are freely available from Companies House.
Open Corporates provides company information from across the world, but it is not free in bulk.
Some analysis of the geographical clustering of software companies in the UK.
This project requires students with some data analysis skills.
AI startup ecosystem
AI has exploded on the tech scene, and lots of people are creating startups to build services/products around LLMs. Teams are very fluid, with people moving around a lot looking for a viable service/product. Sometimes these teams form companies, and these might eventually leave stealth mode and become visible. What are the characteristics of the AI startup ecosystem within a city; questions include: how many people are working within it, their backgrounds, and the business areas are they focusing on?
This project requires students with people skills and a willingness to get out and about. Much of the current AI ecosystem is only visible to those within it. Evening meetups and workshops offer a way into this personal network. This research involves bootstrapping the data gathering by spending evenings schmoozing with founders and their new hires, and is probably only practical in major cities with a very active tech meeting scene.
An analysis of a Dutch software business network.
Theoretical analysis
Those with an interest in theory might like to analyse cost-benefit decision-making within software development. Examples of simple analysis+supporting data include:
Analysis of when refactoring becomes cost-effective, and Cost-effectiveness decision for fixing a known coding mistake, and Break even ratios for development investment decisions
Career progression: an invisible issue in software development
Career progression is an important issue in the development of some software systems, but its impact is rarely discussed, let along researched. A common consequence of career progression is that a project looses a member of staff, e.g., they move to work on a different project, or leave the company. Hiring staff and promoting staff are related neglected research areas.
Understanding the initial and ongoing development of non-trivial software systems requires an understanding of the career progression, and expectations of progression, of the people working on the system.
Effectively working on a software system requires some amount of knowledge of how it operates, or is intended to operate. The loss of a person with working knowledge of a system reduces the rate at which a project can be further developed. It takes time to find a suitable replacement, and for that person’s knowledge of the behavior of the existing system to reach a workable level.
We know that most software is short-lived, but know almost nothing about the involvement-lifetime of those who work on software systems.
There has been some research studying the durations over which people have been involved with individual Open source projects. However, I don’t believe the findings from this research, because I think that non-paid involvement on an Open source project has very different duration and motivation characteristics than a paying job (there are also data cleaning issues around the same person using multiple email addresses, and people working in small groups with one person submitting code).
Detailed employment data, in bulk, has commercial value, and so is rarely freely available. It is possible to scrape data from the adverts of job websites, but this only provides information about the kinds of jobs available, not the people employed.
LinkedIn contains lots of detailed employment history, and the US courts have ruled that it is not illegal to scrape this data. It’s on my list of things to do, and I keep an eye out for others making such data available.
The National Longitudinal Survey of Youth has followed the lives of 10k+ people since 1979 (people were asked to detail their lives in periodic surveys). Using this data, Joseph, Boh, Ang, and Slaughter investigated the job categories within the career paths of 500 people who had worked in a technical IT role. The plot below shows the career paths of people who had spent at least five years working in an IT role (code+data):
Employment history provides an upper bound for the time that a person is likely to have worked on a project (being employed to work on an Open source project while, over time, working at multiple companies is an edge case).
A company may have employees simultaneously working on multiple projects, spending a percentage of their time on each. How big a resource impact is the loss of such a person? Were they simply the same kind of cog in multiple projects, or did they play an important synchronization role across projects? Details on all the projects a person worked on would help answer some questions.
Building a software system involves a lot more than writing the code. Technical managers working on high level, broad brush, issues. The project knowledge that technical managers have contributes to ongoing work, and the impact of loosing a technical manager is probably more of a longer term issue than loosing a coding-developer.
There are systems that are developed and maintained by essentially one person over many years. These get written about and celebrated, but are comparatively rare.
One of the more reliable ways of estimating developer productivity is to measure the impact of them leaving a project.
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