Deep Learning-Based Fault Localization with Contextual Information

Zhuo ZHANG  Yan LEI  Qingping TAN  Xiaoguang MAO  Ping ZENG  Xi CHANG  

IEICE TRANSACTIONS on Information and Systems   Vol.E100-D   No.12   pp.3027-3031
Publication Date: 2017/12/01
Publicized: 2017/09/08
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2017EDL8143
Type of Manuscript: LETTER
Category: Software Engineering
fault localization,  dynamic slice,  deep learning,  contextual information,  

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Fault localization is essential for solving the issue of software faults. Aiming at improving fault localization, this paper proposes a deep learning-based fault localization with contextual information. Specifically, our approach uses deep neural network to construct a suspiciousness evaluation model to evaluate the suspiciousness of a statement being faulty, and then leverages dynamic backward slicing to extract contextual information. The empirical results show that our approach significantly outperforms the state-of-the-art technique Dstar.