Pre-training of an artificial neural network for software fault prediction

Owhadi-Kareshk, Moein; Sedaghat, Yasser; Akbarzadeh-T, Mohammad-R.

doi:10.1109/iccke.2017.8167880

Cited by 8 publications

(1 citation statement)

References 35 publications

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“…There has been much research work [26, 27] going on over SDP over the past three decades. Naive Bayes [28–31], support vector machine (SVM) [32–34], classification and regression trees [35, 36], AdaBoost [37–39], random forest [40–42], artificial neural network as defect prediction [43, 44], and development effort estimation [45]. Multilayer perceptrons (MLPs) [46, 47] are the most widely used classifiers in defect prediction.…”

Section: Related Workmentioning

confidence: 99%

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

2020

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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.

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