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

• URL: http://arxiv.org/abs/2510.04796v1
• Date: Mon, 06 Oct 2025 13:22:10 GMT
• Title: RevMine: An LLM-Assisted Tool for Code Review Mining and Analysis Across Git Platforms
• Authors: Samah Kansab, Francis Bordeleau, Ali Tizghadam,
• Abstract summary: RevMine streamlines the entire code review mining pipeline using large language models (LLMs)<n>It guides users through authentication, endpoint discovery, and natural language-driven data collection.<n>It supports both quantitative and qualitative analysis based on user-defined filters or LLM-inferred patterns.
• Score: 1.2744523252873348
• License: http://creativecommons.org/publicdomain/zero/1.0/
• 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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