arXiv AI By Rishi Raj, Aashray Tandon, Nikolay Matyunin, Jibesh Patra

ComBeautify: Leveraging Code Change Summaries for Conventional Commit Generation

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ComBeautify is a tool that automatically generates Conventional Commits Specification (CCS)-compliant commit messages by combining context-preserving summarization of Git diffs with large language models. It learns commit categories from historical data rather than relying on manually curated definitions, and also produces the CCS description component. Evaluation on 13 languages, 6,500 repositories, and over 20 million commits shows that project-specific training can outperform generic baselines by up to three times, with summarization accuracy reaching 95.25% on a manually curated dataset.

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arXiv Machine Learning
Oct 22

Code Comprehensor: Hierarchical Documentation and Cross-Modal Retrieval for Large Repositories

Code Comprehensor is a hybrid retrieval‑reasoning framework designed to aid understanding of large codebases. It combines a code dependency callgraph, CodeScribe—which generates hierarchical documentation using code‑aware LLMs—and CodeContrast, a CLIP‑style dual‑encoder that learns aligned text‑code embeddings for fast, semantically precise retrieval. Experiments on OpenCV and a 5M‑LOC proprietary repository show that CodeScribe‑derived supervision improves cross‑modal retrieval accuracy, and LLM‑based evaluation reports over 90% faithfulness.

By Sujoy Roychowdhury, Joseph H. R. Isaac, Jiangning Gao, Martin Klitte, Diarmuid Corcoran