Nana Zhang1, Kun Zhu1, Shi Ying1, *, Xu Wang2
CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 279-308, 2020, DOI:10.32604/cmc.2020.011001
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
(defective or non-defective). (2) we… More >
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