Analyzing Women’s Contributions to Open-Source Software Projects based on Large Language Models

Open-source software (OSS) enables users to access, modify, distribute software based on open-source licenses, serving as vital digital infrastructure. Notably, GitHub stands out as a prominent OSS community, with 94 million developers engaged in projects by 2022. However, accurately assessing women’s contributions in OSS encounters challenges due to limited gender data. To address this, we propose an innovative method that employs the Large-Language-Model (LLM), ChatLM2. This LLM-based approach allows cross-lingual analysis of women’s involvement and quantitatively assesses their impact on OSS projects. The study aims to uncover gender disparities and encourage greater participation of female developers in the open-source realm. The article is structured with sections on research methods, design, LLM-based gender detection, women’s participation, impact assessment, implications, and future research.