Empirical assessment of feature selection techniques in defect prediction models using web applications

Article type: Research Article

Authors: Malhotra, Ruchika | Sharma, Anjali^{; *}

Affiliations: [1] Department of Computer Science and Engineering, Delhi Technological University, Delhi, India

Correspondence: [*] Corresponding author. A. Sharma, Department of Computer Science and Engineering, Delhi Technological University, Delhi, India. E-mail: anjali@nplindia.org.

Abstract:  In order to minimize the over-fitting and related factors that are caused by the high dimensionality of the input data in software defect prediction, the attributes are often optimized using various feature selection techniques. However, the comparative performance of these selection techniques in combination with machine learning algorithms remains largely unexplored using web applications. In this work, we investigate the best possible combination of feature selection technique with machine learning algorithms, with the sample space chosen from open source Apache Click and Rave data sets. Our results are based on 945 defect prediction models derived from parametric, non-parametric and ensemble-based machine learning algorithms, for which the metrics are derived from the various filter and threshold-based ranking techniques. Friedman and Nemenyi post-hoc statistical tests are adopted to identify the performance difference of these models. We find that filter-based feature selection in combination with ensemble-based machine learning algorithms not only poise as the best strategy but also yields a maximum feature set redundancy by 94%, with little or no comprise on the performance index.

Keywords: Feature selection, feature ranking, machine learning, web application quality

DOI: 10.3233/JIFS-18473

Journal: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 6, pp. 6567-6578, 2019