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ASE 2021
Mon 15 - Fri 19 November 2021 Australia

This program is tentative and subject to change.

Wed 17 Nov 2021 11:20 - 11:40 at Kangaroo - Defects (any Band 1)

Just-In-Time (JIT) defect prediction (i.e., an AI/ML model to predict defect-introducing commits) is proposed to help developers prioritize their limited Software Quality Assurance (SQA) resources on the most risky commits. However, the explainability of JIT defect models remains largely unexplored (i.e., practitioners still do not know why a commit is predicted as defect-introducing). Recently, LIME has been used to generate explanations for any AI/ML models. However, the random perturbation approach used by LIME to generate synthetic neighbors is still suboptimal, i.e., generating synthetic neighbors that may not be similar to an instance to be explained, producing low accuracy of the local models, leading to inaccurate explanations for just-in-time defect models.

In this paper, we propose PyExplainer—i.e., a local rule-based model-agnostic technique for generating explanations (i.e., why a commit is predicted as defective) of JIT defect models. Through a case study of two open-source software projects, we find that our PyExplainer produces (1) synthetic neighbors that are 41%-45% more similar to an instance to be explained; (2) 18%-38% more accurate local models; and (3) explanations that are 69%-98% more unique and 17%-54% more consistent with the actual characteristics of defect-introducing commits in the future than LIME (a state-of-the-art model-agnostic technique). This could help practitioners focus on the most important aspects of the commits to mitigate the risk of being defect-introducing. Thus, the contributions of this paper build an important step towards Explainable AI for Software Engineering, making software analytics more explainable and actionable. Finally, we publish our PyExplainer as a Python package to support practitioners and researchers.

This program is tentative and subject to change.

Wed 17 Nov

Displayed time zone: Hobart change

11:00 - 12:00
11:00
20m
Talk
An Extensive Study on Smell-Aware Bug Localization
Journal-first Papers
Aoi Takahashi Tokyo Institute of Technology, Natthawute Sae-Lim Tokyo Institute of Technology, Shinpei Hayashi Tokyo Institute of Technology, Motoshi Saeki Nanzan University
Link to publication DOI
11:20
20m
Talk
PyExplainer: Explaining the Predictions of Just-In-Time Defect ModelsACM Distinguished Paper
Research Papers
Chanathip Pornprasit Monash University, Chakkrit Tantithamthavorn Monash University, Jirayus Jiarpakdee Monash University, Australia, Micheal Fu Monash University, Patanamon Thongtanunam University of Melbourne
11:40
20m
Talk
PyNose: A Test Smell Detector For Python
Research Papers
Tongjie Wang University of California, Irvine, Yaroslav Golubev JetBrains Research, Oleg Smirnov JetBrains Research, Saint Petersburg State University, Jiawei Li University of California, Irvine, Timofey Bryksin JetBrains Research; HSE University, Iftekhar Ahmed University of California, Irvine
Pre-print