Just-in-time defect prediction on JavaScript projects: A replication study

• Main Authors: NI, Chao, XIA, Xin, LO, David, YANG, Xiaohu, HASSAN, Ahmed E.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: Defect Prediction; Just-in-time Defect Prediction; Empirical Study; JavaScript; Databases and Information Systems; Programming Languages and Compilers; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/7663; https://ink.library.smu.edu.sg/context/sis_research/article/8666/viewcontent/tosem_2020_0133.pdf
• Institution: Singapore Management University

Change-level defect prediction is widely referred to as just-in-time (JIT) defect prediction since it identifies a defect-inducing change at the check-in time, and researchers have proposed many approaches based on the language-independent change-level features. These approaches can be divided into two types: supervised approaches and unsupervised approaches, and their effectiveness has been verified on Java or C++ projects. However, whether the language-independent change-level features can effectively identify the defects of JavaScript projects is still unknown. Additionally, many researches have confirmed that supervised approaches outperform unsupervised approaches on Java or C++ projects when considering inspection effort. However, whether supervised JIT defect prediction approaches can still perform best on JavaScript projects is still unknown. Lastly, prior proposed change-level features are programming language–independent, whether programming language–specific change-level features can further improve the performance of JIT approaches on identifying defect-prone changes is also unknown.To address the aforementioned gap in knowledge, in this article, we collect and label the top-20 most starred JavaScript projects on GitHub. JavaScript is an extremely popular and widely used programming language in the industry. We propose five JavaScript-specific change-level features and conduct a large-scale empirical study (i.e., involving a total of 176,902 changes) and find that (1) supervised JIT defect prediction approaches (i.e., CBS+) still statistically significantly outperform unsupervised approaches on JavaScript projects when considering inspection effort; (2) JavaScript-specific change-level features can further improve the performance of approach built with language-independent features on identifying defect-prone changes; (3) the change-level features in the dimension of size (i.e., LT), diffusion (i.e., NF), and JavaScript-specific (i.e., SO and TC) are the most important features for indicating the defect-proneness of a change on JavaScript projects; and (4) project-related features (i.e., Stars, Branches, Def Ratio, Changes, Files, Defective, and Forks) have a high association with the probability of a change to be a defect-prone one on JavaScript projects.