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arXiv 2606.08096cs.SE

识别OSS项目中的唯一开发者:一系列模型

Identifying unique developers in OSS projects: A family of models

University of Oulu(奥卢大学);University of Southern Denmark(南丹麦大学)

Ruoyu Su, Alexander Bakhtin, Matteo Esposito, Davide Taibi, Valentina Lenarduzzi

AI总结:

针对开源项目中开发者别名导致耦合度量失真问题,提出基于Indel相似度和LLM辅助匹配的流水线,训练并比较多种机器学习模型,以平衡精度与计算开销。

摘要:

组织和逻辑耦合度量需要可靠地识别唯一开发者。在开源软件中,提交元数据仅限于姓名和电子邮件,同一开发者可能以多个别名出现,如果缺少去重,则会扭曲耦合度量。我们旨在构建一个可扩展且准确的开源开发者去重流水线,并提供基于精度与计算开销选择模型的指导。我们使用Indel相似度作为基线,然后运行LLM辅助匹配过程并进行手动验证,以创建重复身份的大型数据集。利用该数据集,我们训练并比较了不同复杂度的经典机器学习模型,评估精度以及训练和推理时间与能耗。我们期望获得一个高质量的数据集和一系列方法的基准,阐明哪些解决方案在大型开源挖掘中提供了精度与成本之间的最佳权衡。

英文摘要:

Organizational and logical coupling metrics require reliable identification of unique developers. In OSS, commit metadata is limited to names and emails, and the same developer may appear under multiple aliases, which can distort coupling measurements if de-duplication is missing. We aim to build a scalable and accurate pipeline for OSS developer de-duplication and to provide guidance on choosing a model based on precision vs. computational effort. We use Indel similarity as a baseline, then run an LLM-assisted matching process with manual validation to create a large dataset of duplicate identities. Using this dataset, we train and compare classical ML models of different complexity, evaluating precision along with training and inference time and energy. We expect a high-quality dataset and a benchmark of approaches that clarifies which solutions offer the best trade-off between accuracy and cost for large-scale OSS mining.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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