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Software defect prediction is a typical imbalance learning problem, in which the number of ‘True’ (defective) examples is far less than that of ‘False’ (Non-defective) ones. The minority class is often the learning objective. However, the existing researchers devote on building the prediction model on the static metric-based software defect data set, focusing on how to balance the imbalance class distribution, but ignoring the effect from the discrimination ability of features in the context of the software defect data set. Therefore, a dissimilarity-based software defect prediction algorithm (DSDPA) is proposed,which can be used for effectively increasing the performance of software defect prediction.