Presentation 2009-01-20
A Comparative Study of Fault-Prone Module Detection Methods : Fault-proneness Filtering and Logistic Regression
Huahao LIU, Osamu MIZUNO, Tohru KIKUNO,
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Abstract(in English) Prediction of fault-prone software modules has been one of the most classical and important area of software engineering so far. We have proposed a novel approach for predicting fault-prone modules using a spam filtering technique, named Fault-proneness filtering (FPF). In our approach, fault-prone modules are detected in a way that the source code modules are considered as text files and are applied to the spam filter directly. Recently, an another approach using code churn metrics has been proposed. This method is based on a logistic regression model and using metrics related to code churn (the amount of modified code between revisions). In this paper, we conduct a comparative study of fault-proneness filtering and code churn based logistic regression model using the data of an open source software development. Furthermore, we propose a method to integrate these two methods. The method is also evaluated by the same data.
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Keyword(in English) Fault-prone module / text classification / logistic regression / revision history
Paper # KBSE2008-47
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Committee KBSE
Conference Date 2009/1/12(1days)
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Registration To Knowledge-Based Software Engineering (KBSE)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) A Comparative Study of Fault-Prone Module Detection Methods : Fault-proneness Filtering and Logistic Regression
Sub Title (in English)
Keyword(1) Fault-prone module
Keyword(2) text classification
Keyword(3) logistic regression
Keyword(4) revision history
1st Author's Name Huahao LIU
1st Author's Affiliation Graduate School of Information Science and Technology, Osaka University()
2nd Author's Name Osamu MIZUNO
2nd Author's Affiliation Graduate School of Information Science and Technology, Osaka University
3rd Author's Name Tohru KIKUNO
3rd Author's Affiliation Graduate School of Information Science and Technology, Osaka University
Date 2009-01-20
Paper # KBSE2008-47
Volume (vol) vol.108
Number (no) 384
Page pp.pp.-
#Pages 6
Date of Issue