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CAMGen: A Core Change-Aware Multi-View Framework for Commit Message Generation

Bo Xiong, Weisheng Chen, Zongen Ren, Yifeng Zhu, Xutian Li, Runbang Yan, Chong Wang, Peng Liang · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.31552 · cited by 0 works (OpenAlex) · open-access PDF

Korean

커미트 메시지 컨텍스트는 개발자가 코드 변경을 이해하고 소프트웨어 유지 보수를 지원하는 데 도움이됩니다. 최근의 접근 방식은 커미트 메시지 생성 (CMG)를 대형 언어 모델 (LLM)로 개선하기 위해 저장소 컨텍스트를 사용합니다. 그러나 기존의 CMG 방법은 로컬 편집이 여러 코드 엔터테인먼트에 의해 공동으로 구현되는 기능에 영향을 미치는 방법을 이해하는 데 제한된 지원을 제공합니다. 이 기능적 이해는 주요 변경과 그 영향을 설명하는 커미트 메시지를 생성하는 데 필수적입니다. 이 문제를 해결하기 위해 CAMGen을 제안합니다. 이것은 영향을받는 기능적 컨텍스트를 생성하는 데 필수적인 커미트 메시지 생성 프레임 워크입니다. CAMGen은 기본

Machine translation (Meta M2M100); the English original follows.

Abstract

Commit messages help developers understand code changes and support software maintenance. Recent approaches use repository context to improve commit message generation (CMG) with large language models (LLMs). However, existing CMG methods provide limited support for understanding how local edits affect functionality jointly implemented by multiple code entities. This functional understanding is essential for generating commit messages that explain the main change and its impact. To address this problem, we propose CAMGen, a core change-aware multi-view commit message generation framework that incorporates functional context into generation. CAMGen identifies core entities that represent the primary change intent. It then forms candidate functional units through repository-wide semantic clustering and uses those containing the core entities to construct a Functional Semantic View. This view supplies semantic context for interpreting the affected functionality. CAMGen also uses context provided by the Structural Dependency View and the Historical Exemplar View. The context from all three views is then provided to the LLM together with the code diff to guide CMG. The experimental results show that CAMGen outperforms representative CMG baselines across three programming languages. Human evaluation further shows that CAMGen generates more complete commit messages with clearer explanations of change rationale and impact. These results demonstrate the value of functional context and complementary repository information for CMG.

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Topics: Commit, Computer science, Context (archaeology), Core (optical fiber), Semantics (computer science), Construct (python library)

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