An under-sampled software defect prediction method based on hybrid multi-objective cuckoo search

Both the problem of class imbalance in datasets and parameter selection of Support Vector Machine (SVM) are crucial to predict software defects. However, there is no one working to solve these problems synchronously at present. To tackle this problem, a hybrid multi-objective cuckoo search under-sampled software defect prediction model based on SVM (HMOCS-US-SVM) is proposed to solve synchronously above two problems. Firstly, a hybrid multi-objective cuckoo search with dynamical local search (HMOCS) is utilized to select synchronously the non-defective sampling and optimize the parameters of SVM. Then, three under-sampled methods for decision region range are proposed to select the non-defective modules. In the simulation, the three indicators, including the false positive rate (pf), the probability of detection (pd), and G-mean, are employed to measure the performance of the proposed algorithm. In addition, eight datasets from Promise database are selected to verify the proposed software defect predication model. Comparing with the result of eight prediction models, the proposed method comes into effect on solving software defect prediction problem.