Recovering Fine-Grained Code Change Rationale from Multiple Software Artifacts
Understanding the reasons behind past code changes is critical for refactoring, code review, and debugging. However, code change rationale is often fragmented, inconsistently documented, and scattered across heterogeneous artifacts. We address this challenge with two contributions. First, we conduct an empirical study of nine rationale components from an established taxonomy and trace where they are documented across artifacts associated with 63 commits from five widely used open-source Java projects. Seven components appear in practice, and rationale is highly fragmented: commit messages and pull requests primarily capture GOAL, while NEED and ALTERNATIVE are more often found in issues and pull requests. No single artifact type consistently captures all components, which presents the need for cross-document reasoning. Second, we introduce ARGUS, an LLM-based approach that identifies sentences expressing GOAL, NEED, and ALTERNATIVE across a commit's artifacts and synthesizes them into concise rationale summaries. ARGUS achieved 51.4% overall precision and 93.2% recall for rationale identification and generated summaries rated as accurate relative to reference summaries. Experiments across different LLMs showed varying identification performance but consistently accurate summaries. A user study with 12 Java programmers found these summaries useful for understanding unfamiliar code changes and supporting code review, documentation, debugging, and maintenance.