Skip to Main content Skip to Navigation
Conference papers

Software Metrics Reduction for Fault-Proneness Prediction of Software Modules

Abstract : It would be valuable to use metrics to identify the fault-proneness of software modules. However, few research works are on how to select appropriate metrics for fault-proneness prediction currently. We conduct a large-scale comparative experiment of nine different software metrics reduction methods over eleven public-domain data sets from the NASA metrics data repository. The Naive Bayes data miner, with a log-filtering preprocessor on the numeric data, is utilized to construct the prediction model. Comparisons are based on the analysis of variance. Our conclusion is that, reduction methods of software metrics are important to build adaptable and robust software fault-proneness prediction models. Given our results on Naive Bayes and log-filtering, discrete wavelet transformation outperforms other reduction methods, and correlation-based feature selection with genetic search algorithm and information gain can also obtain better predicted performance.
Document type :
Conference papers
Complete list of metadata

Cited literature [16 references]  Display  Hide  Download
Contributor : Hal Ifip Connect in order to contact the contributor
Submitted on : Monday, August 11, 2014 - 9:33:34 AM
Last modification on : Friday, August 11, 2017 - 5:44:10 PM
Long-term archiving on: : Wednesday, November 26, 2014 - 9:30:32 PM


Files produced by the author(s)


Distributed under a Creative Commons Attribution 4.0 International License



Yunfeng Luo, Kerong Ben, Lei Mi. Software Metrics Reduction for Fault-Proneness Prediction of Software Modules. IFIP International Conference on Network and Parallel Computing (NPC), Sep 2010, Zhengzhou, China. pp.432-441, ⟨10.1007/978-3-642-15672-4_36⟩. ⟨hal-01054966⟩



Record views


Files downloads