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.