Identifying unique developers in OSS projects: A family of models

2026-06-06

AI Summary

This work addresses the challenge of accurately disambiguating developer identities in open-source software projects, where rampant use of aliases severely undermines the reliability of organizational and logical coupling metrics. To tackle this issue, the authors propose a scalable, high-precision developer identity deduplication pipeline that, for the first time, integrates large language model (LLM)-assisted annotation with human verification to construct a large-scale, high-quality dataset of duplicate identities. Building upon this dataset, they systematically evaluate the trade-offs among accuracy, inference time, and energy consumption across a range of classical machine learning models. The study contributes both the publicly released dataset and comprehensive benchmarking results, offering practical guidance for selecting cost-effective and accurate deduplication strategies tailored to diverse application scenarios.

Abstract

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.

Problem

• developer de-duplication
• open source software
• commit metadata
• identity resolution
• software mining

Innovation

• developer de-duplication
• LLM-assisted matching
• organizational coupling
• scalable identity resolution
• precision-cost trade-off