Paper Type
Short
Abstract
Bots are increasingly being utilized in open-source platforms like GitHub to enhance efficiency and productivity. However, contributors can abuse bots on these platforms to gain undue reputation and career benefits. Such abuse has negative implications for the quality, transparency, and fairness of these environments. This motivates us to develop a framework for the detection of abusive bot contributions (commits). The framework is applied to a dataset of 10,000 life science repositories on GitHub to detect abusive versus authentic contributions. We also examine the correlations between abusive contributions and popularity or reputational indicators such as stars and forks. Our study highlights that stars and forks (considered as proxy indicators of repository quality) may fail to capture authentic contributions. The framework and findings offer initial theoretical, methodological, and practical implications for open-source environments. We also outline several directions for future research based on this work.
Recommended Citation
Shahid, Mahnoor; Subasinghage, Maduka; Reichman, Shachar; and Kankanhalli, Atreyi, "Detecting Bot-based Abuse and its Impacts on Open-Source Platforms" (2024). ICIS 2024 Proceedings. 4.
https://aisel.aisnet.org/icis2024/paperathon/paperathon/4
Detecting Bot-based Abuse and its Impacts on Open-Source Platforms
Bots are increasingly being utilized in open-source platforms like GitHub to enhance efficiency and productivity. However, contributors can abuse bots on these platforms to gain undue reputation and career benefits. Such abuse has negative implications for the quality, transparency, and fairness of these environments. This motivates us to develop a framework for the detection of abusive bot contributions (commits). The framework is applied to a dataset of 10,000 life science repositories on GitHub to detect abusive versus authentic contributions. We also examine the correlations between abusive contributions and popularity or reputational indicators such as stars and forks. Our study highlights that stars and forks (considered as proxy indicators of repository quality) may fail to capture authentic contributions. The framework and findings offer initial theoretical, methodological, and practical implications for open-source environments. We also outline several directions for future research based on this work.
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