- Title
- A Collaboration-Aware Approach to Profiling Developer Expertise with Cross-Community Data
- Creator
- Song, Xiaotao; Yan, Jiafei; Huang, Yuexin; Sun, Hailong; Zhang, Hongyu
- Relation
- 2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS). Proceedings of the 22nd IEEE International Conference on Software Quality, Reliability and Security (QRS) (Guangzhou, China 05-09 December, 2022) p. 344-355
- Publisher Link
- http://dx.doi.org/10.1109/QRS57517.2022.00043
- Publisher
- Institute of Electrical and Electronic Engineers (IEEE)
- Resource Type
- conference paper
- Date
- 2022
- Description
- Developer expertise is an important factor that should be considered in various software development activities. And it is challenging to accurately profile the expertise of developers as their activities often disperse across different online communities, such as Community Question Answering sites (e.g., Stack Overflow) and Open Source Software platforms (e.g., GitHub). In this regard, early work mainly considers a single community while recent studies are starting to profile developers with cross-community data. However, few works consider the collaborative interactions among developers in evaluating developer expertise across communities. In this work, we propose a collaboration-aware approach to profiling developer expertise using cross-community data by taking into consideration developers’ contributions, collaborative interactions, and the dynamic changes of expertise. Specifically, we are concerned with the common developers in GitHub and Stack Overflow. First, we propose a time-sensitive model to characterize the developer’s expertise in the two communities and integrate the results to generate basic expertise profiles. Second, we build a developer network by analyzing the collaborative interactions among the developers of the two communities. Finally, we apply the topic-sensitive PageRank algorithm to incorporate developer relationships into expertise profiling. Results of extensive experiments on a large number of common developers of GitHub and Stack Overflow demonstrate the effectiveness of our approach.
- Subject
- developer expertise; developer network; topic-sensitive PageRank; GitHub; stack overflow
- Identifier
- http://hdl.handle.net/1959.13/1493530
- Identifier
- uon:53570
- Identifier
- ISBN:9781665477055
- Language
- eng
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