一句话：提出可解释的即时缺陷预测框架，并作为 GitHub 应用接入 CI/CD，自动反馈提交风险、原因与缓解建议。
即时（JIT）缺陷预测通过历史数据训练分类模型，预测引入缺陷的提交，但已有研究指出其预测缺乏可解释性，且因未融入 CI/CD 与 GitHub 等现代开发平台而难以落地。本文提出一个可解释的 JIT 缺陷预测框架：对每次提交自动给出风险程度、解释其为何有风险，并建议风险缓解方案；该框架以 GitHub 应用形式接入 GitHub CI/CD 流水线，可持续监控和分析多个仓库的提交流。作者还讨论了实践者的使用场景与启示，并提供视频演示。摘要未报告定量评估结果。
Just-In-Time (JIT) defect prediction is a classification model that is trained using historical data to predict bug-introducing changes. However, recent studies raised concerns related to the explainability of the predictions of many software analytics applications (i.e., practitioners do not understand why commits are risky and how to improve them). In addition, the adoption of Just-In-Time defect prediction is still limited due to a lack of integration into CI/CD pipelines and modern software development platforms (e.g., GitHub). In this paper, we present an explainable Just-In-Time defect prediction framework to automatically generate feedback to developers by providing the riskiness of each commit, explaining why such commit is risky, and suggesting risk mitigation plans. The proposed framework is integrated into the GitHub CI/CD pipeline as a GitHub application to continuously monitor and analyse a stream of commits in many GitHub repositories. Finally, we discuss the usage scenarios and their implications to practitioners. The VDO demonstration is available at https://jitbot-tool.github.io/
DOI 原文 ·
@inproceedings{paperbot5901,
title = {JITBot},
author = {Chaiyakarn Khanan and Worawit Luewichana and Krissakorn Pruktharathikoon and Jirayus Jiarpakdee and Chakkrit Tantithamthavorn and Morakot Choetkiertikul and Chaiyong Ragkhitwetsagul and Thanwadee Sunetnanta},
booktitle = {ASE},
year = {2020},
doi = {10.1145/3324884.3415295}
}