The invention provides a defect high-risk module identification method based on a
software network, and belongs to the field of
software complex networks. The method comprises the steps that 1, constructing an adaptive classifier, whrein the adaptive classifier comprises a plurality of classifiers; step 2, performing adaptive
feature selection; step 3, performing adaptive threshold optimization; step 4, optimizing the internal parameters of the adaptive classifier; and 5, selecting a self-adaptive optimal prediction model, and performing defect high-risk module identification on the to-be-tested
software network by using the optimal prediction model. For any type of defect
data set, contents in five aspects of construction of the self-adaptive classifier, self-adaptive
feature selection, self-adaptive threshold optimization, self-adaptive classifier internal parameter tuning, selection of a self-adaptive optimal prediction model and the like can be completed according to the characteristics of a
data set, the best defect prediction result is obtained, and a high-risk software module is identified.