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A Heuristic Rule Reduction Approach to Software Fault-proneness Prediction

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dc.contributor.author Monden, Akito en
dc.contributor.author Keung, Jacky en
dc.contributor.author Morisaki, Shuji en
dc.contributor.author Kamei, Yasutaka en
dc.contributor.author Matsumoto, Ken-Ichi en
dc.date.accessioned 2018-10-30T04:57:15Z en
dc.date.available 2018-10-30T04:57:15Z en
dc.date.issued 2012 en
dc.identifier.isbn 9781467349307 en
dc.identifier.issn 1530-1362 en
dc.identifier.uri http://hdl.handle.net/10061/12746 en
dc.description 2012 19th Asia-Pacific Software Engineering Conference, 4-7 Dec. 2012, Hong Kong, China en
dc.description.abstract Background: Association rules are more comprehensive and understandable than fault-prone module predictors (such as logistic regression model, random forest and support vector machine). One of the challenges is that there are usually too many similar rules to be extracted by the rule mining. Aim: This paper proposes a rule reduction technique that can eliminate complex (long) and/or similar rules without sacrificing the prediction performance as much as possible. Method: The notion of the method is to removing long and similar rules unless their confidence level as a heuristic is high enough than shorter rules. For example, it starts with selecting rules with shortest length (length=1), and then it continues through the 2nd shortest rules selection (length=2) based on the current confidence level, this process is repeated on the selection for longer rules until no rules are worth included. Result: An empirical experiment has been conducted with the Mylyn and Eclipse PDE datasets. The result of the Mylyn dataset showed the proposed method was able to reduce the number of rules from 1347 down to 13, while the delta of the prediction performance was only. 015 (from. 757 down to. 742) in terms of the F1 prediction criteria. In the experiment with Eclipsed PDE dataset, the proposed method reduced the number of rules from 398 to 12, while the prediction performance even improved (from. 426 to. 441.) Conclusion: The novel technique introduced resolves the rule explosion problem in association rule mining for software proneness prediction, which is significant and provides better understanding of the causes of faulty modules. en
dc.language.iso en en
dc.publisher IEEE en
dc.rights c Copyright IEEE 2012 en
dc.subject data mining en
dc.subject software fault tolerance en
dc.subject heuristic rule reduction approach en
dc.subject software fault-proneness prediction en
dc.subject logistic regression model en
dc.subject random forest en
dc.subject support vector machine en
dc.subject fault-prone module predictor en
dc.subject rule reduction technique en
dc.subject Mylyn dataset en
dc.subject Eclipse PDE dataset en
dc.subject association rule mining en
dc.subject Measurement en
dc.subject Association rules en
dc.subject Explosions en
dc.subject Software en
dc.subject Predictive models en
dc.subject Educational institutions en
dc.subject defect prediction en
dc.subject empirical study en
dc.subject association rule mining en
dc.subject data mining en
dc.subject software quality en
dc.title A Heuristic Rule Reduction Approach to Software Fault-proneness Prediction en
dc.type.nii Conference Paper en
dc.textversion author en
dc.identifier.spage 838 en
dc.identifier.epage 847 en
dc.relation.doi 10.1109/APSEC.2012.103 en
dc.identifier.NAIST-ID 73292310 en

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