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Tue 10 Nov 2020 08:05 - 08:06 at Virtual room 2 - Empirical in Practice

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.

Tue 10 Nov
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08:00 - 08:30: Empirical in PracticePaper Presentations / Industry Papers / Journal First at Virtual room 2
08:00 - 08:02
Talk
Industry Papers
Shinobu SaitoNTT Software Innovation Center, IIMURA YukakoNTT, Japan, Emad AghayiGeorge Mason University, Thomas LaTozaGeorge Mason University, USA
DOI
08:03 - 08:04
Talk
Journal First
Vahid GarousiQueen's University Belfast, Görkem GirayIndependent Researcher, Eray TüzünBilkent University, Çağatay ÇatalWageningen University, Michael FeldererUniversity of Innsbruck
08:05 - 08:06
Talk
Industry Papers
Meng YanSchool of Big Data & Software Engineering, Chongqing University, Xin XiaMonash University, Yuanrui FanZhejiang University, David LoSingapore Management University, Ahmed E. HassanQueen's University, Xindong ZhangAlibaba Group
DOI
08:07 - 08:08
Talk
Industry Papers
He ZhangNanjing University, Huang HuangState Key Laboratory of Novel Software Technology, Software Institute, Nanjing University, Dong ShaoNanjing University, Xin Huang
DOI
08:09 - 08:10
Talk
Industry Papers
Lin ShiInstitute of Software at Chinese Academy of Sciences, China, Mingyang LiInstitute of Software at Chinese Academy of Sciences, China, Mingzhe XingISCAS, Yawen WangISCAS, Qing WangInstitute of Software, Chinese Academy of Sciences, Xinhua PengChina Merchants Bank, China, Weimin LiaoChina Merchants Bank, China, Guizhen PiChina Merchants Bank, China, Haiqing WangBeijing Software Cost Evaluation Technology Innovation Alliance, China
DOI
08:11 - 08:12
Talk
Paper Presentations
Orges CicoNorwegian University of Science and Technology
08:13 - 08:30
Talk
Paper Presentations
Huang HuangState Key Laboratory of Novel Software Technology, Software Institute, Nanjing University, Rachel Tzoref-BrillIBM Research, Sebastian Baltes QAware GmbH and The University of Adelaide, Shinobu SaitoNTT Software Innovation Center, M: Diomidis SpinellisAthens University of Economics and Business