Title: An empirical analysis of the statistical learning models for different categories of cross-project defect prediction

Authors: Lipika Goel; Mayank Sharma; Sunil Kumar Khatri; D. Damodaran

Addresses: Amity Institute of Information Technology, Amity University Noida, Uttar Pradesh 201313, India; AKG Engineering College, Uttar Pradesh 201009, India ' Amity Institute of Information Technology, Amity University Noida, Uttar Pradesh 201313, India ' Amity Institute of Information Technology, Amity University Noida, Uttar Pradesh 201313, India ' Centre for Reliability, STQC Directorate, Department of Electronics and IT, Ministry of Communications and IT, Dr, VSI, Estate, Thiruvanmiyur, Chennai, India

Abstract: Currently, the research community is addressing the problem of defect prediction with the availability of project defect data. The availability of different project data leads to extend the research on cross projects. Cross-project defect prediction has now become an accepted area of software project management. In this paper, an empirical study is carried out to investigate the predictive performance of availability within project and cross-project defect prediction models. Furthermore, different categories of cross-project data are taken for training and testing to analyse various statistical models. In this paper data models are analysed and compared using various statistical performance measures. The findings during the empirical analysis of the data models state that gradient boosting predictor outperforms in the cross-project defect prediction scenario. Results also infer that cross-project defect prediction is comparable with project defect prediction and has statistical significance.

Keywords: defect prediction; cross projects; within-project; machine learning; cross validation; supervised learning; classification; training dataset; quality assurance; homogeneous metrics.

DOI: 10.1504/IJCAET.2021.113549

International Journal of Computer Aided Engineering and Technology, 2021 Vol.14 No.2, pp.233 - 254

Received: 13 Jan 2018
Accepted: 13 Jul 2018

Published online: 03 Mar 2021 *

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