Kun Zhu1, Nana Zhang1, Qing Zhang2, Shi Ying1, *, Xu Wang3
CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1467-1486, 2020, DOI:10.32604/cmc.2020.011415
Abstract Software defect prediction plays a very important role in software quality
assurance, which aims to inspect as many potentially defect-prone software modules as
possible. However, the performance of the prediction model is susceptible to high
dimensionality of the dataset that contains irrelevant and redundant features. In addition,
software metrics for software defect prediction are almost entirely traditional features
compared to the deep semantic feature representation from deep learning techniques. To
address these two issues, we propose the following two solutions in this paper: (1) We
leverage a novel non-linear manifold learning method - SOINN Landmark Isomap (SLIsomap) to extract the… More >
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