IEICE TRANSACTIONS on Information
Prediction of Fault-Prone Software Modules Using a Generic Text Discriminator
Summary :
This paper describes a novel approach for detecting fault-prone modules using a spam filtering technique. Fault-prone module detection in source code is important for the assurance of software quality. Most previous fault-prone detection approaches have been based on using software metrics. Such approaches, however, have difficulties in collecting the metrics and constructing mathematical models based on the metrics. Because of the increase in the need for spam e-mail detection, the spam filtering technique has progressed as a convenient and effective technique for text mining. In our approach, fault-prone modules are detected in such a way that the source code modules are considered text files and are applied to the spam filter directly. To show the applicability of our approach, we conducted experimental applications using source code repositories of Java based open source developments. The result of experiments shows that our approach can correctly predict 78% of actual fault-prone modules as fault-prone.
- Publication
- IEICE TRANSACTIONS on Information Vol.E91-D No.4 pp.888-896
- Publication Date
- 2008/04/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1093/ietisy/e91-d.4.888
- Type of Manuscript
- Special Section PAPER (Special Section on Knowledge-Based Software Engineering)
- Category
- Software Engineering
Authors
Keyword
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Osamu MIZUNO, Tohru KIKUNO, "Prediction of Fault-Prone Software Modules Using a Generic Text Discriminator" in IEICE TRANSACTIONS on Information,
vol. E91-D, no. 4, pp. 888-896, April 2008, doi: 10.1093/ietisy/e91-d.4.888.
Abstract: This paper describes a novel approach for detecting fault-prone modules using a spam filtering technique. Fault-prone module detection in source code is important for the assurance of software quality. Most previous fault-prone detection approaches have been based on using software metrics. Such approaches, however, have difficulties in collecting the metrics and constructing mathematical models based on the metrics. Because of the increase in the need for spam e-mail detection, the spam filtering technique has progressed as a convenient and effective technique for text mining. In our approach, fault-prone modules are detected in such a way that the source code modules are considered text files and are applied to the spam filter directly. To show the applicability of our approach, we conducted experimental applications using source code repositories of Java based open source developments. The result of experiments shows that our approach can correctly predict 78% of actual fault-prone modules as fault-prone.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e91-d.4.888/_p
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@ARTICLE{e91-d_4_888,
author={Osamu MIZUNO, Tohru KIKUNO, },
journal={IEICE TRANSACTIONS on Information},
title={Prediction of Fault-Prone Software Modules Using a Generic Text Discriminator},
year={2008},
volume={E91-D},
number={4},
pages={888-896},
abstract={This paper describes a novel approach for detecting fault-prone modules using a spam filtering technique. Fault-prone module detection in source code is important for the assurance of software quality. Most previous fault-prone detection approaches have been based on using software metrics. Such approaches, however, have difficulties in collecting the metrics and constructing mathematical models based on the metrics. Because of the increase in the need for spam e-mail detection, the spam filtering technique has progressed as a convenient and effective technique for text mining. In our approach, fault-prone modules are detected in such a way that the source code modules are considered text files and are applied to the spam filter directly. To show the applicability of our approach, we conducted experimental applications using source code repositories of Java based open source developments. The result of experiments shows that our approach can correctly predict 78% of actual fault-prone modules as fault-prone.},
keywords={},
doi={10.1093/ietisy/e91-d.4.888},
ISSN={1745-1361},
month={April},}
Copy
TY - JOUR
TI - Prediction of Fault-Prone Software Modules Using a Generic Text Discriminator
T2 - IEICE TRANSACTIONS on Information
SP - 888
EP - 896
AU - Osamu MIZUNO
AU - Tohru KIKUNO
PY - 2008
DO - 10.1093/ietisy/e91-d.4.888
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E91-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2008
AB - This paper describes a novel approach for detecting fault-prone modules using a spam filtering technique. Fault-prone module detection in source code is important for the assurance of software quality. Most previous fault-prone detection approaches have been based on using software metrics. Such approaches, however, have difficulties in collecting the metrics and constructing mathematical models based on the metrics. Because of the increase in the need for spam e-mail detection, the spam filtering technique has progressed as a convenient and effective technique for text mining. In our approach, fault-prone modules are detected in such a way that the source code modules are considered text files and are applied to the spam filter directly. To show the applicability of our approach, we conducted experimental applications using source code repositories of Java based open source developments. The result of experiments shows that our approach can correctly predict 78% of actual fault-prone modules as fault-prone.
ER -
English
