Paper Type
Complete
Abstract
Open source software (OSS) relies on user participation for success. Yet, many OSS projects overlook community feedback (issues), leaving its impact on project success underexplored. We examine how responding to and resolving feedback affect project popularity and code reuse. Applying a difference-in-differences approach on a comprehensive dataset of 69,041 GitHub projects and 250,417 issues, we find that a project’s first feedback response increases popularity by 16.4% and code reuse by 12.3%, while the first feedback resolution boosts popularity by 22.8% and code reuse by 13.3%. Additional analyses show that addressing issues more promptly strengthens these effects. Using semi-supervised learning, we classify feedback types and find that prioritizing bug reports and enhancement requests yields more benefits than addressing general inquiries. The positive impact is also stronger for projects that are less mature, more user-oriented and have fewer prior contributions.
Paper Number
1829
Recommended Citation
Wang, Zisu; Ge, Yong; and Youn, Seokjun, "Your Issues Are My Command: How Addressing Community Feedback Affects Open Source Software Project Success" (2025). AMCIS 2025 Proceedings. 6.
https://aisel.aisnet.org/amcis2025/it_pm/it_pm/6
Your Issues Are My Command: How Addressing Community Feedback Affects Open Source Software Project Success
Open source software (OSS) relies on user participation for success. Yet, many OSS projects overlook community feedback (issues), leaving its impact on project success underexplored. We examine how responding to and resolving feedback affect project popularity and code reuse. Applying a difference-in-differences approach on a comprehensive dataset of 69,041 GitHub projects and 250,417 issues, we find that a project’s first feedback response increases popularity by 16.4% and code reuse by 12.3%, while the first feedback resolution boosts popularity by 22.8% and code reuse by 13.3%. Additional analyses show that addressing issues more promptly strengthens these effects. Using semi-supervised learning, we classify feedback types and find that prioritizing bug reports and enhancement requests yields more benefits than addressing general inquiries. The positive impact is also stronger for projects that are less mature, more user-oriented and have fewer prior contributions.
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