TFIDF-FL: Localizing Faults Using Term Frequency-Inverse Document Frequency and Deep Learning
Summary :
Existing fault localization based on neural networks utilize the information of whether a statement is executed or not executed to identify suspicious statements potentially responsible for a failure. However, the information just shows the binary execution states of a statement, and cannot show how important a statement is in executions. Consequently, it may degrade fault localization effectiveness. To address this issue, this paper proposes TFIDF-FL by using term frequency-inverse document frequency to identify a high or low degree of the influence of a statement in an execution. Our empirical results on 8 real-world programs show that TFIDF-FL significantly improves fault localization effectiveness.
- Publication
- IEICE TRANSACTIONS on Information Vol.E102-D No.9 pp.1860-1864
- Publication Date
- 2019/09/01
- Publicized
- 2019/05/27
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2018EDL8237
- Type of Manuscript
- LETTER
- Category
- Software Engineering
Authors
Zhuo ZHANG
National University of Defense Technology
Yan LEI
Chongqing University
Jianjun XU
National University of Defense Technology
Xiaoguang MAO
National University of Defense Technology
Xi CHANG
National University of Defense Technology
Keyword
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Zhuo ZHANG, Yan LEI, Jianjun XU, Xiaoguang MAO, Xi CHANG, "TFIDF-FL: Localizing Faults Using Term Frequency-Inverse Document Frequency and Deep Learning" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 9, pp. 1860-1864, September 2019, doi: 10.1587/transinf.2018EDL8237.
Abstract: Existing fault localization based on neural networks utilize the information of whether a statement is executed or not executed to identify suspicious statements potentially responsible for a failure. However, the information just shows the binary execution states of a statement, and cannot show how important a statement is in executions. Consequently, it may degrade fault localization effectiveness. To address this issue, this paper proposes TFIDF-FL by using term frequency-inverse document frequency to identify a high or low degree of the influence of a statement in an execution. Our empirical results on 8 real-world programs show that TFIDF-FL significantly improves fault localization effectiveness.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDL8237/_p
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@ARTICLE{e102-d_9_1860,
author={Zhuo ZHANG, Yan LEI, Jianjun XU, Xiaoguang MAO, Xi CHANG, },
journal={IEICE TRANSACTIONS on Information},
title={TFIDF-FL: Localizing Faults Using Term Frequency-Inverse Document Frequency and Deep Learning},
year={2019},
volume={E102-D},
number={9},
pages={1860-1864},
abstract={Existing fault localization based on neural networks utilize the information of whether a statement is executed or not executed to identify suspicious statements potentially responsible for a failure. However, the information just shows the binary execution states of a statement, and cannot show how important a statement is in executions. Consequently, it may degrade fault localization effectiveness. To address this issue, this paper proposes TFIDF-FL by using term frequency-inverse document frequency to identify a high or low degree of the influence of a statement in an execution. Our empirical results on 8 real-world programs show that TFIDF-FL significantly improves fault localization effectiveness.},
keywords={},
doi={10.1587/transinf.2018EDL8237},
ISSN={1745-1361},
month={September},}
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TY - JOUR
TI - TFIDF-FL: Localizing Faults Using Term Frequency-Inverse Document Frequency and Deep Learning
T2 - IEICE TRANSACTIONS on Information
SP - 1860
EP - 1864
AU - Zhuo ZHANG
AU - Yan LEI
AU - Jianjun XU
AU - Xiaoguang MAO
AU - Xi CHANG
PY - 2019
DO - 10.1587/transinf.2018EDL8237
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2019
AB - Existing fault localization based on neural networks utilize the information of whether a statement is executed or not executed to identify suspicious statements potentially responsible for a failure. However, the information just shows the binary execution states of a statement, and cannot show how important a statement is in executions. Consequently, it may degrade fault localization effectiveness. To address this issue, this paper proposes TFIDF-FL by using term frequency-inverse document frequency to identify a high or low degree of the influence of a statement in an execution. Our empirical results on 8 real-world programs show that TFIDF-FL significantly improves fault localization effectiveness.
ER -
English
