OSS Mentor A framework for improving developers contributions via deep reinforcement learning
This addresses a gap in open source governance by enabling developers to enhance their contributions, though it appears incremental as it applies known techniques to a new domain.
The paper tackles the problem of improving developers' contributions in open source projects by introducing OSS Mentor, a deep reinforcement learning framework trained from empirical knowledge, which significantly outperforms existing methods in experiments.
In open source project governance, there has been a lot of concern about how to measure developers' contributions. However, extremely sparse work has focused on enabling developers to improve their contributions, while it is significant and valuable. In this paper, we introduce a deep reinforcement learning framework named Open Source Software(OSS) Mentor, which can be trained from empirical knowledge and then adaptively help developers improve their contributions. Extensive experiments demonstrate that OSS Mentor significantly outperforms excellent experimental results. Moreover, it is the first time that the presented framework explores deep reinforcement learning techniques to manage open source software, which enables us to design a more robust framework to improve developers' contributions.