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A Heuristic Rule Reduction Approach to Software Fault-proneness Prediction
http://hdl.handle.net/10061/12746
http://hdl.handle.net/10061/127462dee0e65-1358-45b7-b0d4-dad7453fb10e
名前 / ファイル | ライセンス | アクション |
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fulltext (631.4 kB)
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Item type | 会議発表論文 / Conference Paper(1) | |||||
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公開日 | 2018-10-30 | |||||
タイトル | ||||||
タイトル | A Heuristic Rule Reduction Approach to Software Fault-proneness Prediction | |||||
言語 | ||||||
言語 | eng | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | data mining | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | software fault tolerance | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | heuristic rule reduction approach | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | software fault-proneness prediction | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | logistic regression model | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | random forest | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | support vector machine | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | fault-prone module predictor | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | rule reduction technique | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | Mylyn dataset | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | Eclipse PDE dataset | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | association rule mining | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | Measurement | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | Association rules | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | Explosions | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | Software | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | Predictive models | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | Educational institutions | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | defect prediction | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | empirical study | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | association rule mining | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | data mining | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | software quality | |||||
資源タイプ | ||||||
資源タイプ | conference paper | |||||
アクセス権 | ||||||
アクセス権 | open access | |||||
著者 |
Monden, Akito
× Monden, Akito× Keung, Jacky× Morisaki, Shuji× Kamei, Yasutaka× Matsumoto, Ken-Ichi |
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抄録 | ||||||
内容記述タイプ | Abstract | |||||
内容記述 | 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. | |||||
書誌情報 |
p. 838-847, 発行日 2012 |
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会議情報 | ||||||
会議名 | 2012 19th Asia-Pacific Software Engineering Conference | |||||
開催期間 | 4-7 Dec. 2012, | |||||
開催地 | Hong Kong | |||||
開催国 | CHN | |||||
出版者 | ||||||
出版者 | IEEE | |||||
ISSN | ||||||
収録物識別子タイプ | ISSN | |||||
収録物識別子 | 1530-1362 | |||||
ISBN | ||||||
識別子タイプ | ISBN | |||||
関連識別子 | 9781467349307 | |||||
DOI | ||||||
関連タイプ | isVersionOf | |||||
識別子タイプ | DOI | |||||
関連識別子 | https://doi.org/10.1109/APSEC.2012.103 | |||||
権利 | ||||||
権利情報 | c Copyright IEEE 2012 | |||||
著者版フラグ | ||||||
出版タイプ | AM |