Newest papers

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

Chinese

承诺消息背景有助于开发人员理解代码变更并支持软件维护。最近的方法使用存储环境来改进承诺消息生成(CMG)与大型语言模型(LLMs)。然而,现有的CMG方法为了解本地编辑如何影响多个代码实体共同实施的功能提供了有限的支持。 这种功能理解对于生成解释主要变更及其影响的承诺消息至关重要。 为了解决这个问题,我们提出CAMGen,一个核心变更意识的多观点承诺消息生成框架,该框架将功能背景纳入生成中。 CAMGen识别了代表主要变更意图的核心实体。 然后它通过存储库广泛的语义分类生成候选功能单位,并使用包含核心实体的内容构建一个功能性语义视图。 此视图

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.

Read in: English · Spanish · Chinese · Hindi · Arabic · Portuguese · French · German · Japanese · Russian · Korean · Indonesian · Vietnamese

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

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange