文献类型：期刊文献

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 University of Science and Technology, Shanghai, China

年份：2022

起止页码：202

外文期刊名：ACM International Conference Proceeding Series

收录：EI(收录号：20224212894305)

语种：英文

外文关键词：Program debugging - Social aspects - Software engineering - Tracking (position)

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. ? 2022 Association for Computing Machinery.