Title: Statistical assessment of nonlinear manifold detection-based software defect prediction techniques

Authors: Soumi Ghosh; Ajay Rana; Vineet Kansal

Addresses: Department of Computer Science and Engineering, Amity University Uttar Pradesh, Noida, Uttar Pradesh, India ' Department of Computer Science and Engineering, Amity University Uttar Pradesh, Noida, Uttar Pradesh, India ' Department of Computer Science and Engineering, Institute of Engineering and Technology, Lucknow, Uttar Pradesh, India

Abstract: Prediction of software defects has immense importance for obtaining desired outcome at minimised cost and so attracted researchers working on this topic applying various techniques, which were not found fully effective. Software datasets comprise of redundant features that hinder effective application of techniques resulting inappropriate defect prediction. Hence, it requires newer application of nonlinear manifold detection techniques (nonlinear MDTs) that has been examined for accurate prediction of defects at lesser time and cost using different classification techniques. In this work, we analysed and tested the effect of nonlinear MDTs to find out accurate and best classification technique for all datasets. Comparison has been made between the results of without or with nonlinear MDTs and paired two-tailed T-test has been performed for statistical testing and verifying the performance of classifiers using nonlinear MDTs on all datasets. Outcome revealed that among all nonlinear MDTs, FastMVU makes most accurate prediction of software defects.

Keywords: dimensionality reduction; fast maximum variance unfolding; FastMVU; machine learning; manifold detection; nonlinear; promise repository; software defect prediction.

DOI: 10.1504/IJISTA.2019.102667

International Journal of Intelligent Systems Technologies and Applications, 2019 Vol.18 No.6, pp.579 - 605

Received: 22 Aug 2017
Accepted: 01 May 2018
Published online: 01 Oct 2019 *

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