JITBot: an explainable Just-In-Time defect prediction bot

Chaiyakarn Khanan, Worawit Luewichana, Krissakorn Pruktharathikoon, Jirayus Jiarpakdee, Chakkrit Tantithamthavorn, Morakot Choetkiertikul, Chaiyong Ragkhitwetsagul, Thanwadee Sunetnanta

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

2 Citations (Scopus)

Abstract

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/.

Original languageEnglish
Title of host publicationProceedings - 2020 35th IEEE/ACM International Conference on Automated Software Engineering, ASE 2020
EditorsClaire Le Goues, David Lo
Place of PublicationNew York NY USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1336-1339
Number of pages4
ISBN (Electronic)9781450367684
DOIs
Publication statusPublished - 2020
EventAutomated Software Engineering Conference 2020 - Virtual, Melbourne, Australia
Duration: 21 Sep 202025 Sep 2020
Conference number: 35th
https://dl.acm.org/doi/proceedings/10.1145/3324884 (Proceedings)
https://conf.researchr.org/home/ase-2020 (Website)
https://dl.acm.org/doi/proceedings/10.1145/3417113 (Proceedings)

Conference

ConferenceAutomated Software Engineering Conference 2020
Abbreviated titleASE 2020
Country/TerritoryAustralia
CityMelbourne
Period21/09/2025/09/20
Internet address

Keywords

  • Just-In-Time Defect Prediction
  • Software Quality Assurance

Cite this