A universal data augmentation approach for fault localization-Reference-Cited by-同舟云学术

A universal data augmentation approach for fault localization

Published:2022-05-21 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 44th International Conference on Software Engineering
language:
Short-container-title:

Author:

Xie Huan¹,Lei Yan¹,Yan Meng¹,Yu Yue²,Xia Xin³,Mao Xiaoguang²

Affiliation:

1. Chongqing University, Chongqing, China

2. National University of Defense Technology, Changsha, China

3. Technology Lab, Huawei, China

Funder

the National Natural Science Foundation of China

the Fundamental Research Funds for the Central Universities

the Natural Science Foundation of Chongqing

the National Key Research and Development Project of China

the National Defense Basic Scientific Research Project

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3510003.3510136

Reference72 articles.

1. An Evaluation of Similarity Coefficients for Software Fault Localization

2. Spectrum-Based Multiple Fault Localization

3. Understanding of a convolutional neural network

4. Antreas Antoniou , Amos Storkey , and Harrison Edwards . 2017. Data augmentation generative adversarial networks. arXiv preprint arXiv:1711.04340 ( 2017 ). Antreas Antoniou, Amos Storkey, and Harrison Edwards. 2017. Data augmentation generative adversarial networks. arXiv preprint arXiv:1711.04340 (2017).

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