IEICE TRANSACTIONS on Information
Improving the Accuracy of Spectrum-Based Fault Localization Using Multiple Rules
Summary :
Software fault localization, as one of the essential activities in program debugging, aids to software developers to identify the locations of faults in a program, thus reducing the cost of program debugging. Spectrum-based fault localization (SBFL), as one of the representative localization techniques, has been intensively studied. The localization technique calculates the probability of each program entity that is faulty by a certain suspiciousness formula. The accuracy of SBFL is not always as satisfactory as expected because it neglects the contextual information of statement executions. Therefore, we proposed 5 rules, i.e., random, the maximum coverage, the minimum coverage, the maximum distance, and the minimum distance, to improve the accuracy of SBFL for further. The 5 rules can effectively use the contextual information of statement executions. Moreover, they can be implemented on the traditional SBFL techniques using suspiciousness formulas with little effort. We empirically evaluated the impacts of the rules on 17 suspiciousness formulas. The results show that all 5 rules can significantly improve the ranking of faulty statements. Particularly, for the faults difficult to locate, the improvement is more remarkable. Generally, the rules can effectively reduce the number of statements examined by an average of more than 19%. Compared with other rules, the minimum coverage rule generates better results. This indicates that the application of the test case having the minimum coverage capability for fault localization is more effective.
- Publication
- IEICE TRANSACTIONS on Information Vol.E103-D No.6 pp.1328-1338
- Publication Date
- 2020/06/01
- Publicized
- 2020/02/26
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2019EDP7207
- Type of Manuscript
- PAPER
- Category
- Software Engineering
Authors
Rongcun WANG
China University of Mining and Technology,Mining Digitization Engineering Research Center of Ministry of Education of the People's Republic of China
Shujuan JIANG
China University of Mining and Technology
Kun ZHANG
China University of Mining and Technology
Qiao YU
Jiangsu Normal University
Keyword
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Rongcun WANG, Shujuan JIANG, Kun ZHANG, Qiao YU, "Improving the Accuracy of Spectrum-Based Fault Localization Using Multiple Rules" in IEICE TRANSACTIONS on Information,
vol. E103-D, no. 6, pp. 1328-1338, June 2020, doi: 10.1587/transinf.2019EDP7207.
Abstract: Software fault localization, as one of the essential activities in program debugging, aids to software developers to identify the locations of faults in a program, thus reducing the cost of program debugging. Spectrum-based fault localization (SBFL), as one of the representative localization techniques, has been intensively studied. The localization technique calculates the probability of each program entity that is faulty by a certain suspiciousness formula. The accuracy of SBFL is not always as satisfactory as expected because it neglects the contextual information of statement executions. Therefore, we proposed 5 rules, i.e., random, the maximum coverage, the minimum coverage, the maximum distance, and the minimum distance, to improve the accuracy of SBFL for further. The 5 rules can effectively use the contextual information of statement executions. Moreover, they can be implemented on the traditional SBFL techniques using suspiciousness formulas with little effort. We empirically evaluated the impacts of the rules on 17 suspiciousness formulas. The results show that all 5 rules can significantly improve the ranking of faulty statements. Particularly, for the faults difficult to locate, the improvement is more remarkable. Generally, the rules can effectively reduce the number of statements examined by an average of more than 19%. Compared with other rules, the minimum coverage rule generates better results. This indicates that the application of the test case having the minimum coverage capability for fault localization is more effective.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019EDP7207/_p
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@ARTICLE{e103-d_6_1328,
author={Rongcun WANG, Shujuan JIANG, Kun ZHANG, Qiao YU, },
journal={IEICE TRANSACTIONS on Information},
title={Improving the Accuracy of Spectrum-Based Fault Localization Using Multiple Rules},
year={2020},
volume={E103-D},
number={6},
pages={1328-1338},
abstract={Software fault localization, as one of the essential activities in program debugging, aids to software developers to identify the locations of faults in a program, thus reducing the cost of program debugging. Spectrum-based fault localization (SBFL), as one of the representative localization techniques, has been intensively studied. The localization technique calculates the probability of each program entity that is faulty by a certain suspiciousness formula. The accuracy of SBFL is not always as satisfactory as expected because it neglects the contextual information of statement executions. Therefore, we proposed 5 rules, i.e., random, the maximum coverage, the minimum coverage, the maximum distance, and the minimum distance, to improve the accuracy of SBFL for further. The 5 rules can effectively use the contextual information of statement executions. Moreover, they can be implemented on the traditional SBFL techniques using suspiciousness formulas with little effort. We empirically evaluated the impacts of the rules on 17 suspiciousness formulas. The results show that all 5 rules can significantly improve the ranking of faulty statements. Particularly, for the faults difficult to locate, the improvement is more remarkable. Generally, the rules can effectively reduce the number of statements examined by an average of more than 19%. Compared with other rules, the minimum coverage rule generates better results. This indicates that the application of the test case having the minimum coverage capability for fault localization is more effective.},
keywords={},
doi={10.1587/transinf.2019EDP7207},
ISSN={1745-1361},
month={June},}
Copy
TY - JOUR
TI - Improving the Accuracy of Spectrum-Based Fault Localization Using Multiple Rules
T2 - IEICE TRANSACTIONS on Information
SP - 1328
EP - 1338
AU - Rongcun WANG
AU - Shujuan JIANG
AU - Kun ZHANG
AU - Qiao YU
PY - 2020
DO - 10.1587/transinf.2019EDP7207
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E103-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 2020
AB - Software fault localization, as one of the essential activities in program debugging, aids to software developers to identify the locations of faults in a program, thus reducing the cost of program debugging. Spectrum-based fault localization (SBFL), as one of the representative localization techniques, has been intensively studied. The localization technique calculates the probability of each program entity that is faulty by a certain suspiciousness formula. The accuracy of SBFL is not always as satisfactory as expected because it neglects the contextual information of statement executions. Therefore, we proposed 5 rules, i.e., random, the maximum coverage, the minimum coverage, the maximum distance, and the minimum distance, to improve the accuracy of SBFL for further. The 5 rules can effectively use the contextual information of statement executions. Moreover, they can be implemented on the traditional SBFL techniques using suspiciousness formulas with little effort. We empirically evaluated the impacts of the rules on 17 suspiciousness formulas. The results show that all 5 rules can significantly improve the ranking of faulty statements. Particularly, for the faults difficult to locate, the improvement is more remarkable. Generally, the rules can effectively reduce the number of statements examined by an average of more than 19%. Compared with other rules, the minimum coverage rule generates better results. This indicates that the application of the test case having the minimum coverage capability for fault localization is more effective.
ER -
English
