IEICE TRANSACTIONS on Information
Deep Learning-Based Fault Localization with Contextual Information
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information 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
Authors
Zhuo ZHANG
National University of Defense Technology
Yan LEI
Ministry of Education,Chongqing University,Logistical Engineering University
Qingping TAN
National University of Defense Technology
Xiaoguang MAO
National University of Defense Technology
Ping ZENG
National University of Defense Technology
Xi CHANG
National University of Defense Technology
Keyword
Latest Issue
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Zhuo ZHANG, Yan LEI, Qingping TAN, Xiaoguang MAO, Ping ZENG, Xi CHANG, "Deep Learning-Based Fault Localization with Contextual Information" in IEICE TRANSACTIONS on Information,
vol. E100-D, no. 12, pp. 3027-3031, December 2017, doi: 10.1587/transinf.2017EDL8143.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2017EDL8143/_p
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@ARTICLE{e100-d_12_3027,
author={Zhuo ZHANG, Yan LEI, Qingping TAN, Xiaoguang MAO, Ping ZENG, Xi CHANG, },
journal={IEICE TRANSACTIONS on Information},
title={Deep Learning-Based Fault Localization with Contextual Information},
year={2017},
volume={E100-D},
number={12},
pages={3027-3031},
abstract={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.},
keywords={},
doi={10.1587/transinf.2017EDL8143},
ISSN={1745-1361},
month={December},}
Copy
TY - JOUR
TI - Deep Learning-Based Fault Localization with Contextual Information
T2 - IEICE TRANSACTIONS on Information
SP - 3027
EP - 3031
AU - Zhuo ZHANG
AU - Yan LEI
AU - Qingping TAN
AU - Xiaoguang MAO
AU - Ping ZENG
AU - Xi CHANG
PY - 2017
DO - 10.1587/transinf.2017EDL8143
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E100-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2017
AB - 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.
ER -
English
