Evaluating Static and Process Evidence for Code Authorship in Programming Education
TL;DR
Comparisons show that measured signal depends on production context and that process patterns can complement weak final-code signal in educational repositories, and that such models are appropriate only as instructor-mediated decision support, not as independent proof of authorship.
Abstract
In programming courses, instructors may need to interpret whether a submission is consistent with a student's prior programming profile, especially when code similarity alone is inconclusive. Existing source-code authorship methods are often evaluated on programming-contest or open-source datasets, where reusable templates and local code patterns can produce strong author-related signal. Educational repositories present a different setting. Students solve shared assignments while their programming practices are still developing. This study uses task-aware evaluation to contrast these production contexts and tests whether repository-visible process features add information beyond final code in six matched educational comparisons. Contest data provide a high-signal contrast, with a Kick Start mean top-1 of 0.938. Educational datasets produce substantially lower attribution performance. Adding process features raises the educational mean from 0.094 to 0.233 and mean pairwise verification ROC-AUC from 0.556 to 0.752. The comparisons show that measured signal depends on production context and that process patterns can complement weak final-code signal in educational repositories. Such models are therefore appropriate only as instructor-mediated decision support, not as independent proof of authorship.