A Network Clustering Based Software Attribute Selection for Identifying Fault-Prone Modules

The software defect can damage the reliability and the quality of the software. The static code software metrics have been widely used and played an important role in software defect prediction. Instead of using whole features, it is quite necessary to remove the redundant features and select some meaningful features to improve the prediction performance. This study focuses on the effective attribute selection technique for the software fault classification. We proposed the software attributes network that indicates the mutual information between features and the clustering based attribute selection techniques. The results demonstrate that the proposed network clustering based feature selection performs the best on fault-prone modules prediction. The comparative feature selection techniques are examined to evaluate the result. Furthermore, the best-performed software attributes and the relations between them are shown and carefully analyzed.

[1]  Maurice H. Halstead,et al.  Elements of software science (Operating and programming systems series) , 1977 .

[2]  Carsten O. Daub,et al.  The mutual information: Detecting and evaluating dependencies between variables , 2002, ECCB.

[3]  S. Nickolas,et al.  Feature Selection Using Decision Tree Induction in Class level Metrics Dataset for Software Defect Predictions , 2010 .

[4]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[5]  Sergei Vassilvitskii,et al.  k-means++: the advantages of careful seeding , 2007, SODA '07.

[6]  Michael Mitzenmacher,et al.  Detecting Novel Associations in Large Data Sets , 2011, Science.

[7]  Anas N. Al-Rabadi,et al.  A comparison of modified reconstructability analysis and Ashenhurst‐Curtis decomposition of Boolean functions , 2004 .

[8]  Banu Diri,et al.  Investigating the effect of dataset size, metrics sets, and feature selection techniques on software fault prediction problem , 2009, Inf. Sci..

[9]  Karim O. Elish,et al.  Predicting defect-prone software modules using support vector machines , 2008, J. Syst. Softw..

[10]  Mohammad Alshayeb,et al.  Software defect prediction using ensemble learning on selected features , 2015, Inf. Softw. Technol..

[11]  Lior Rokach,et al.  Clustering Methods , 2005, The Data Mining and Knowledge Discovery Handbook.

[12]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[13]  Taghi M. Khoshgoftaar,et al.  Choosing software metrics for defect prediction: an investigation on feature selection techniques , 2011, Softw. Pract. Exp..

[14]  Darrel C. Ince,et al.  A critique of three metrics , 1994, J. Syst. Softw..

[15]  Chih-Jen Lin,et al.  Combining SVMs with Various Feature Selection Strategies , 2006, Feature Extraction.

[16]  Xiao Liu,et al.  An empirical study on software defect prediction with a simplified metric set , 2014, Inf. Softw. Technol..

[17]  Ayse Basar Bener,et al.  On the relative value of cross-company and within-company data for defect prediction , 2009, Empirical Software Engineering.

[18]  S. P. Lloyd,et al.  Least squares quantization in PCM , 1982, IEEE Trans. Inf. Theory.

[19]  J. H. Ward.  Hierarchical Grouping to Optimize an Objective Function , 1963 .

[20]  Michael I. Jordan,et al.  On Spectral Clustering: Analysis and an algorithm , 2001, NIPS.

[21]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[22]  Bart Baesens,et al.  Benchmarking Classification Models for Software Defect Prediction: A Proposed Framework and Novel Findings , 2008, IEEE Transactions on Software Engineering.