| Summary: | In cross-project software defect prediction,original datasets collected and labeled by humans are often corrupted by noisy,and large distribution differences exist between data of the source project and target project.To address the problem,this paper proposes a two-stage cross-project defect prediction method called CLNI-KMM.During the instance filtering stage,noisy instances are filtered by using the CLNI method.During the instance transfer stage,the KMM algorithm for instance transfer is used to adjust the training weights of instances in the source project.On this basis,a software defect prediction model is built by combining the training data with a small ratio of labeled instances in the target project.Experimental results show that compared with classical cross-project software defect prediction methods TCA,TNB and NNFilter,the proposed method has better prediction performance.Meanwhile,it has stronger stability.
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