For Full-Text PDF, please login, if you are a member of IEICE,|
or go to Pay Per View on menu list, if you are a nonmember of IEICE.
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
Publication Date: 2017/12/01
Online ISSN: 1745-1361
Type of Manuscript: LETTER
Category: Software Engineering
fault localization, dynamic slice, deep learning, contextual information,
Full Text: PDF>>
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.