Nana Zhang¹, Kun Zhu¹, Shi Ying^{1, *}, Xu Wang²

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 279-308, 2020, DOI:10.32604/cmc.2020.011001 - 23 July 2020

Abstract Software defect prediction plays an important role in software quality assurance. However, the performance of the prediction model is susceptible to the irrelevant and redundant features. In addition, previous studies mostly regard software defect prediction as a single objective optimization problem, and multi-objective software defect prediction has not been thoroughly investigated. For the above two reasons, we propose the following solutions in this paper: (1) we leverage an advanced deep neural network—Stacked Contractive AutoEncoder (SCAE) to extract the robust deep semantic features from the original defect features, which has stronger discrimination capacity for different classes… More >

Nana Zhang¹, Kun Zhu¹, Shi Ying^{1, *}, Xu Wang²

CMC-Computers, Materials & Continua, Vol.64, No.1, pp. 471-499, 2020, DOI:10.32604/cmc.2020.010117 - 20 May 2020

Abstract Software defect prediction is a research hotspot in the field of software engineering. However, due to the limitations of current machine learning algorithms, we can’t achieve good effect for defect prediction by only using machine learning algorithms. In previous studies, some researchers used extreme learning machine (ELM) to conduct defect prediction. However, the initial weights and biases of the ELM are determined randomly, which reduces the prediction performance of ELM. Motivated by the idea of search based software engineering, we propose a novel software defect prediction model named KAEA based on kernel principal component analysis… More >

Kun Zhu¹, Nana Zhang¹, Shi Ying^{1, *}, Xu Wang²

CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 891-910, 2020, DOI:10.32604/cmc.2020.08096 - 01 May 2020

Abstract With the continuous expansion of software scale, software update and maintenance have become more and more important. However, frequent software code updates will make the software more likely to introduce new defects. So how to predict the defects quickly and accurately on the software change has become an important problem for software developers. Current defect prediction methods often cannot reflect the feature information of the defect comprehensively, and the detection effect is not ideal enough. Therefore, we propose a novel defect prediction model named ITNB (Improved Transfer Naive Bayes) based on improved transfer Naive Bayesian… More >