Towards Competence-Based Management for Open Source Software Projects

2026-08-06

AI Summary

This study addresses the sustainability challenges faced by open-source software projects due to the attrition of core contributors, a problem exacerbated by the lack of effective mechanisms for accurately assessing the technical capabilities of potential successors. To bridge this gap, we propose the first code-metric-based framework for contributor competency evaluation and task matching. By analyzing source code contribution metrics and integrating machine learning techniques, our approach establishes a reproducible, quantitative model of developer capability. This model not only identifies developers with high potential to assume critical roles but also predicts the competency level required to successfully complete specific tasks. The resulting data-driven insights offer actionable support for talent selection and development, thereby significantly enhancing the resilience and continuity of open-source projects.

Abstract

Contributors to Open Source Software (OSS) projects are vital to maintaining the health of both communities and projects. However, the number of projects experiencing core contributors' disengagement has increased to the point that it risks the projects' survival. Finding new contributors to replace the workforce is challenging and time-intensive due to several factors, including a lack of precise knowledge about potential candidates' competences, which may need to be confirmed through interviews and exams. Previous studies provided indications of the contributor's competences, although they lack depth in understanding competence levels, which can result in poor knowledge about the contributor's capabilities. To address this gap, we assess contributors' competence by collecting code metrics related to source code from contributions. We also propose a competence model able to predict the competence level required to solve tasks. By properly assessing contributors' competences, we can identify and train candidates to replace core contributors. Our replication package, including code, data, and documentation, is available at https://doi.org/10.5281/zenodo.21605122

Problem

• Open Source Software
• contributor disengagement
• competence assessment
• core contributors
• talent replacement

Innovation

• competence assessment
• open source software
• code metrics
• contributor prediction
• competence model