LLM Contribution Summarization in Software Projects
Rafael Corsi Ferrao, Fabio Roberto de Miranda, Diego Pavan Soler

TL;DR
This paper introduces an automated tool using large language models to summarize individual code contributions in team projects, aiming to assist instructors in providing timely feedback and reducing grading effort.
Contribution
It presents a novel combination of LLM prompt engineering with automated repository analysis to evaluate student contributions in software projects.
Findings
The tool effectively summarized student contributions with high accuracy.
Students and faculty found the summaries useful for grading and guidance.
The approach reduced manual effort in assessing individual contributions.
Abstract
This full paper in innovative practice provides an automated tool to summarize individual code contributions in project-based courses with external clients. Real industry projects offer valuable learning opportunities by immersing students in authentic problems defined by external clients. However, the open-ended and highly variable scope of these projects makes it challenging for instructors and teaching assistants to provide timely and detailed feedback. This paper addresses the need for an automated and objective approach to evaluate individual contributions within team projects. In this paper, we present a tool that leverages a large language model (LLM) to automatically summarize code contributions extracted from version control repositories. The tool preprocesses and structures repository data, and uses PyDriller to isolate individual contributions. Its uniqueness lies in the…
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