Improving Fault Localization Using Conditional Variational Autoencoder

Xianmei FANG
Xiaobo GAO
Yuting WANG
Zhouyu LIAO
Yue MA

IEICE TRANSACTIONS on Information and Systems   Vol.E105-D    No.8    pp.1490-1494
Publication Date: 2022/08/01
Publicized: 2022/05/13
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2022EDL8024
Type of Manuscript: LETTER
Category: Software Engineering
debugging,  fault localization,  conditional variational autoencoder,  data augmentation,  

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Fault localization analyzes the runtime information of two classes of test cases (i.e., passing test cases and failing test cases) to identify suspicious statements potentially responsible for a failure. However, the failing test cases are always far fewer than passing test cases in reality, and the class imbalance problem will affect fault localization effectiveness. To address this issue, we propose a data augmentation approach using conditional variational auto-encoder to synthesize new failing test cases for FL. The experimental results show that our approach significantly improves six state-of-the-art fault localization techniques.

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