摘要：To identify software modules that are more likely to be defective, machine learning has been used to construct software defect prediction (SDP) models. However, several previous works have found that the imbalanced nature of software defective data can decrease the model performance. In this paper, we discussed the issue of how to improve imbalanced data distribution in the context of SDP, which can benefit software defect prediction with the aim of finding better methods. Firstly, a relative density was introduced to reflect the significance of each instance within its class, which is irrelevant to the scale of data distribution in feature space; hence, it can be more robust than the absolute distance information. Secondly, a K-nearest-neighbors-based probability density estimation (KNN-PDE) alike strategy was utilised to calculate the relative density of each training instance. Furthermore, the fuzzy memberships of sample were designed based on relative density in order to eliminate classification error coming from noise and outlier samples. Finally, two algorithms were proposed to train software defect prediction models based on the weighted extreme learning machine. This paper compared the proposed algorithms with traditional SDP methods on the benchmark data sets. It was proved that the proposed methods have much better overall performance in terms of the measures including G-mean, AUC, and Balance. The proposed algorithms are more robust and adaptive for SDP data distribution types and can more accurately estimate the significance of each instance and assign the identical total fuzzy coefficients for two different classes without considering the impact of data scale.
