Code, Commits, and Complexity in the GenAI Era: A Longitudinal Study of Team-Based Capstone Projects
Generative AI tools are now widely available to students, but little is known about how team-based project codebases have changed since they appeared. This matters when project courses use repository activity as evidence of progress and learning. Existing studies have largely examined self-reports, controlled tasks, or short individual assignments, leaving open questions around how student codebases evolve in authentic project settings. We present a ten-semester analysis of N=260 capstone team repositories from 2021 to 2025, spanning pre- and post-AI cohorts. We examine three dimensions: code size (lines of code), code characteristics (comment density, error-handling density, and cyclomatic complexity), and development behaviour (commit volume, edit size, and commit frequency). We find that end-of-semester codebases roughly triple, from about 3.4 KLOC in 2021--2023 to about 10 KLOC in 2024--2025. Code characteristics show a clear decoupling: cyclomatic complexity and error-handling density increase, while comment density does not. Later cohorts commit far more often and in greater volume, but individual commit sizes are largely unchanged, suggesting a shift in development rhythm rather than edit granularity. Most changes appear from 2024 onwards and are not uniformly monotonic. We did not directly observe AI use, so we report these as changes that coincide with increased GenAI availability rather than as effects of AI use. Our findings highlight the need to reconsider how student-produced artefacts are used and assessed in project courses.