Chaiyakarn KhananWorawit LuewichanaKrissakorn PruktharathikoonJirayus JiarpakdeeChakkrit TantithamthavornMorakot ChoetkiertikulChaiyong RagkhitwetsagulThanwadee SunetnantaMonash UniversityMahidol University2021-02-032021-02-032020-09-01Proceedings - 2020 35th IEEE/ACM International Conference on Automated Software Engineering, ASE 2020. (2020), 1336-13392-s2.0-85099211017https://repository.li.mahidol.ac.th/handle/20.500.14594/60916© 2020 ACM. 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 explain-ability 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/.Mahidol UniversityComputer ScienceEngineeringConference PaperSCOPUS10.1145/3324884.3415295