Artificial Intelligence in Open Source Software Engineering: A Foundation for Sustainability
S M Rakib UI Karim, Wenyi Lu, Sean Goggins
TL;DR
The paper addresses the sustainability challenges of open-source software by systematically reviewing how artificial intelligence can augment, rather than replace, human collaboration. It surveys AI applications spanning automated maintenance, community health analytics, onboarding, security, and project management, and provides a taxonomy linking AI techniques to OSS sustainability needs. Key contributions include a synthesized map of AI-enabled interventions, a discussion of ethical, governance, and environmental considerations, and identified research gaps such as the need for longitudinal data and standardized sustainability metrics. The work offers practical guidance for researchers and practitioners aiming to build more resilient, equitable OSS ecosystems through responsible AI integration.
Abstract
Open-source software (OSS) is foundational to modern digital infrastructure, yet this context for group work continues to struggle to ensure sufficient contributions in many critical cases. This literature review explores how artificial intelligence (AI) is being leveraged to address critical challenges to OSS sustainability, including maintaining contributor engagement, securing funding, ensuring code quality and security, fostering healthy community dynamics, and preventing project abandonment. Synthesizing recent interdisciplinary research, the paper identifies key applications of AI in this domain, including automated bug triaging, system maintenance, contributor onboarding and mentorship, community health analytics, vulnerability detection, and task automation. The review also examines the limitations and ethical concerns that arise from applying AI in OSS contexts, including data availability, bias and fairness, transparency, risks of misuse, and the preservation of human-centered values in collaborative development. By framing AI not as a replacement but as a tool to augment human infrastructure, this study highlights both the promise and pitfalls of AI-driven interventions. It concludes by identifying critical research gaps and proposing future directions at the intersection of AI, sustainability, and OSS, aiming to support more resilient and equitable open-source ecosystems.