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arXiv · 2510.04796

RevMine: An LLM-Assisted Tool for Code Review Mining and Analysis Across Git Platforms

Samah Kansab·Francis Bordeleau·Ali Tizghadam
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Abstract

Empirical research on code review processes is increasingly central to understanding software quality and collaboration. However, collecting and analyzing review data remains a time-consuming and technically intensive task. Most researchers follow similar workflows - writing ad hoc scripts to extract, filter, and analyze review data from platforms like GitHub and GitLab. This paper introduces RevMine, a conceptual tool that streamlines the entire code review mining pipeline using large language models (LLMs). RevMine guides users through authentication, endpoint discovery, and natural language-driven data collection, significantly reducing the need for manual scripting. After retrieving review data, it supports both quantitative and qualitative analysis based on user-defined filters or LLM-inferred patterns. This poster outlines the tool's architecture, use cases, and research potential. By lowering the barrier to entry, RevMine aims to democratize code review mining and enable a broader range of empirical software engineering studies.

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Samah Kansab, Francis Bordeleau, Ali Tizghadam. 2025-10-06. RevMine: An LLM-Assisted Tool for Code Review Mining and Analysis Across Git Platforms. https://arxiv.org/abs/2510.04796

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