Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
11-2020
Abstract
Effort-aware Just-in-Time (JIT) defect identification aims at identifying defect-introducing changes just-in-time with limited code inspection effort. Such identification has two benefits compared with traditional module-level defect identification, i.e., identifying defects in a more cost-effective and efficient manner. Recently, researchers have proposed various effort-aware JIT defect identification approaches, including supervised (e.g., CBS+, OneWay) and unsupervised approaches (e.g., LT and Code Churn). The comparison of the effectiveness between such supervised and unsupervised approaches has attracted a large amount of research interest. However, the effectiveness of the recently proposed approaches and the comparison among them have never been investigated in an industrial setting.In this paper, we investigate the effectiveness of state-of-the-art effort-aware JIT defect identification approaches in an industrial setting. To that end, we conduct a case study on 14 Alibaba projects with 196,790 changes. In our case study, we investigate three aspects: (1) The effectiveness of state-of-the-art supervised (i.e., CBS+,OneWay, EALR) and unsupervised (i.e., LT and Code Churn) effortaware JIT defect identification approaches on Alibaba projects, (2) the importance of the features used in the effort-aware JIT defect identification approach, and (3) the association between projectspecific factors and the likelihood of a defective change. Moreover, we develop a tool based on the best performing approach and investigate the tool's effectiveness in a real-life setting at Alibaba.
Keywords
Just-in-time defect identification, industrial study, effort-aware
Discipline
Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
ESEC/FSE '20: Proceedings of the 28th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering: 8-13 November, online
First Page
1308
Last Page
1319
ISBN
9781450370431
Identifier
10.1145/3368089.3417048
Publisher
ACM
City or Country
New York
Citation
YAN, Meng; XIA, Xin; FAN, Yuanrui; LO, David; HASSAN, Ahmed E.; and ZHANG, Xindong.
Effort-aware just-in-time defect identification in practice: A case study at Alibaba. (2020). ESEC/FSE '20: Proceedings of the 28th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering: 8-13 November, online. 1308-1319.
Available at: https://ink.library.smu.edu.sg/sis_research/5629
Copyright Owner and License
Publisher
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/3368089.3417048