CNN-FL: An Effective Approach for Localizing Faults using Convolutional Neural Networks

Fault localization aims at identifying suspicious statements potentially responsible for failures. The recent rapid progress on deep learning shows the promising potential of many neural network architectures in making sense of data, and more importantly, this potential offers a new prospective prob...

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Published in2019 IEEE 26th International Conference on Software Analysis, Evolution and Reengineering (SANER) pp. 445 - 455
Main Authors Zhang, Zhuo, Lei, Yan, Mao, Xiaoguang, Li, Panpan
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.02.2019
Subjects
automate fault localization
Convolution
Convolutional neural networks
Deep learning
Kernel
Software engineering
suspiciousness
Training
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DOI10.1109/SANER.2019.8668002

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Summary:Fault localization aims at identifying suspicious statements potentially responsible for failures. The recent rapid progress on deep learning shows the promising potential of many neural network architectures in making sense of data, and more importantly, this potential offers a new prospective probably benefiting fault localization. Thus, this paper proposes CNN-FL: an approach for localizing faults based on convolutional neural networks to explore the promising potential of deep learning in fault localization. Specifically, CNN-FL constructs a convolutional neural network customized for fault localization, and then trains the network with test cases, and finally evaluates the suspiciousness of each statement by testing the trained model using a virtual test set. Our empirical results show that CNN-FL significantly improves fault localization effectiveness.
DOI:10.1109/SANER.2019.8668002