Език: English
Open Source maintainers are increasingly vocal about the effects AI is having on their projects, from overwhelmed developers and overflowing security inboxes to growing numbers of low-quality reports and submissions. Projects have responded in different ways, ranging from restricting who can contribute based on vouching systems to the drastic measure of closing bug trackers and contribution channels. But how much of this pressure is actually measurable?
In this talk, we evaluate whether any of the reported effects leave observable traces in repository data over time. Specifically, we will look at review times, review iterations, pull-request and code churn, issue resolution times and contributor retention. We will also look beyond individual projects and organizations and search for recurring signals across the wider open source ecosystem.
However, every metric has its weaknesses and no signal points to one clear cause. What could be an overeager AI user in one project might be the sole maintainer battling real-life challenges in another. Therefore, we evaluate every metric critically: what influences it, which confounding factors affect it and how much of the observed variation can be connected to the change in the AI landscape. Specifically, we do not try to isolate AI contributions to a project, instead we try to find reflections of systemic changes in the repository data.
Collecting these metrics allows us to connect individual reports from maintainers with quantitative data that can be analyzed across different projects and over time. In practice, we will highlight which signals are worth collecting, what we can infer from them, and which questions repository and contributor data leave unanswered.