NDLI: Software defect prediction using K-PCA and various kernel-based extreme learning machine: an empirical study

Content Provider	IET Digital Library
Author	Pandey, Sushant Kumar Rathee, Deevashwer Tripathi, Anil Kumar
Abstract	Predicting defects during software testing reduces an enormous amount of testing effort and help to deliver a high-quality software system. Owing to the skewed distribution of public datasets, software defect prediction (SDP) suffers from the class imbalance problem, which leads to unsatisfactory results. Overfitting is also one of the biggest challenges for SDP. In this study, the authors performed an empirical study of these two problems and investigated their probable solution. They have conducted 4840 experiments over five different classifiers using eight NASA projects and 14 PROMISE repository datasets. They suggested and investigated the varying kernel function of an extreme learning machine (ELM) along with kernel principal component analysis (K-PCA) and found better results compared with other classical SDP models. They used the synthetic minority oversampling technique as a sampling method to address class imbalance problems and k-fold cross-validation to avoid the overfitting problem. They found ELM-based SDP has a high receiver operating characteristic curve over 11 out of 22 datasets. The proposed model has higher precision and F-score values over ten and nine, respectively, compared with other state-of-the-art models. The Mathews correlation coefficient (MCC) of 17 datasets of the proposed model surpasses other classical models' MCC.
Starting Page	768
Ending Page	782
Page Count	15
ISSN	17518806
Volume Number	14
e-ISSN	17518814
Issue Number	Issue 7, Dec (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-sen.2020.0119
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-sen/14/7
Journal	IET Software
Publisher Date	2020-11-26
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Class Imbalance Problem Classical SDP Model Diagnostic, Testing, Debugging And Evaluating System ELM-based SDP F High Receiver Operating Characteristic Curve High-quality Software System K K-PCA Kernel Function Kernel Principal Component Analysis Kernel-based Extreme Learning Machine Learning in AI Mathews Correlation Coefficient NASA Projects Overfitting Problem Principal Component Analysis Program Testing PROMISE Repository Datasets Public Datasets Sampling Method Software Defect Prediction Software Engineering Software Quality Software Testing Synthetic Minority Oversampling Technique
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design

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