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

© The Institution of Engineering and Technology
Received 30/03/2020, Accepted 10/11/2020, Revised 22/08/2020, Published 26/11/2020

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.

Inspec keywords: software quality; learning (artificial intelligence); sampling methods; program testing; principal component analysis

Other keywords: sampling method; classical SDP models; class imbalance problem; high-quality software system; software testing; public datasets; K; software defect prediction; kernel principal component analysis; ELM-based SDP; high receiver operating characteristic curve; NASA projects; synthetic minority oversampling technique; overfitting problem; F; PROMISE repository datasets; kernel-based extreme learning machine; kernel function; Mathews correlation coefficient; K-PCA

Subjects: Principal component analysis; Software engineering techniques; Machine learning (artificial intelligence); Diagnostic, testing, debugging and evaluating systems

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