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A Heuristic Rule Reduction Approach to Software Fault-proneness Prediction

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dc.contributor.author Monden, Akito
dc.contributor.author Keung, Jacky
dc.contributor.author Morisaki, Shuji
dc.contributor.author Kamei, Yasutaka
dc.contributor.author Matsumoto, Ken-Ichi
dc.date.accessioned 2018-10-30T04:57:15Z
dc.date.available 2018-10-30T04:57:15Z
dc.date.issued 2012
dc.identifier.isbn 9781467349307
dc.identifier.issn 1530-1362
dc.identifier.uri http://hdl.handle.net/10061/12746
dc.description 2012 19th Asia-Pacific Software Engineering Conference, 4-7 Dec. 2012, Hong Kong, China
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.
dc.language.iso en
dc.publisher IEEE
dc.rights c Copyright IEEE 2012
dc.subject data mining
dc.subject software fault tolerance
dc.subject heuristic rule reduction approach
dc.subject software fault-proneness prediction
dc.subject logistic regression model
dc.subject random forest
dc.subject support vector machine
dc.subject fault-prone module predictor
dc.subject rule reduction technique
dc.subject Mylyn dataset
dc.subject Eclipse PDE dataset
dc.subject association rule mining
dc.subject Measurement
dc.subject Association rules
dc.subject Explosions
dc.subject Software
dc.subject Predictive models
dc.subject Educational institutions
dc.subject defect prediction
dc.subject empirical study
dc.subject association rule mining
dc.subject data mining
dc.subject software quality
dc.title A Heuristic Rule Reduction Approach to Software Fault-proneness Prediction
dc.type.nii Conference Paper
dc.identifier.fulltexturl https://doi.org/10.1109/APSEC.2012.103
dc.textversion author
dc.identifier.spage 838
dc.identifier.epage 847
dc.relation.doi 10.1109/APSEC.2012.103
dc.identifier.NAIST-ID 73292310


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