A Universal Data Augmentation Approach for Fault Localization

Huan Xie, Yan Lei, Meng Yan, Yue Yu, Xin Xia, Xiaoguang Mao

Abstract

Data is the fuel to models, and it is still applicable in fault localization (FL). Many existing elaborate FL techniques take the code coverage matrix and failure vector as inputs, expecting the techniques could find the correlation between program entities and failures. However, the input data is high-dimensional and extremely unbalanced since the real-world programs are large in size and the number of failing test cases is much less than that of passing test cases, which are posing severe threats to the effectiveness of FL techniques.

BibTeX

@inproceedings{Xie-al:ICSE22,
  author    = {Huan Xie and
               Yan Lei and
               Meng Yan and
               Yue Yu and
               Xin Xia and
               Xiaoguang Mao},
  title     = {A Universal Data Augmentation Approach for Fault Localization},
  booktitle = {ICSE},
  pages     = {48--60},
  publisher = {{ACM}},
  year      = {2022},
}

A Universal Data Augmentation Approach for Fault Localization

Abstract

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