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

Abstract

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.