文献类型：会议论文

英文题名：Bug Report Priority Prediction Using Developer-Oriented Socio-Technical Features

作者：Huang, Zijie[1];Shao, Zhiqing[1];Fan, Guisheng[1];Yu, Huiqun[1];Yang, Kang[1];Zhou, Ziyi[1]

机构：[1]East China Univ Sci & Technol, Shanghai, Peoples R China

会议论文集：13th Asia-Pacific Symposium on Internetware (Internetware) - A Software Paradigm for Internet Computing

会议日期：JUN 11-12, 2022

会议地点：ELECTR NETWORK

语种：英文

外文关键词：bug report priority; developer sentiment; socio-technical analysis; issue tracking system; empirical software engineering

摘要：Software stakeholders report bugs in Issue Tracking System (ITS) with manually labeled priorities. However, the lack of knowledge and standard for prioritization may cause stakeholders to mislabel the priorities. In response, priority predictors are actively developed to support them. Prior studies trained machine learners based on textual similarity, categorical, and numeric technical features of bug reports. Most models were validated by time-insensitive approaches, and they were producing sub-optimal results for practical usage. Moreover, they tend to ignore the developer and social aspects of ITS. Since ITS bridges users and developers, we integrate their sentiment- and community-oriented socio-technical features to perform 2- and multi-classed bug priority prediction and validate our model in within-project, cross-project, and time-wise scenarios. The proposed model outperforms the 2 baselines by up to 10% in AUC-ROC and 13% in MCC, and the significance of improvement is statistically confirmed. We reveal involving assignee and reporter features from socio-technical perspectives such as sentiment could boost prediction performance. Finally, we test statistically the mean and distribution of the features that reflect the differences in socio-technical aspects (e.g., quality of communication and resource distribution) between high and low priority reports. In conclusion, we suggest researchers should involve contributors' experience and sentiments in bug report priority prediction.