Code, Commits, and Complexity in the GenAI Era: A Longitudinal Study of Team-Based Capstone Projects

Asma Shakil, Claus Brabrand, Paul Denny

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…