Unified integration of many-objective optimization algorithm based on temporary offspring for software defects prediction

Abstract Software defects prediction technology related to software products’ security and quality and provides guidance for software testing. To solve both the problem of datasets class imbalance in software defects prediction and support vector machine (SVM) parameter selection synchronously, high dimension software defects prediction model (HD-SDP) based on SVM is proposed. Including four objectives that the false positive rate of defects, probability of detection, F-metric, and Balance value. And a unified integration of many-objective optimization algorithm based on temporary offspring (UIMaOTO) is designed for this model to select the parameters of SVM and non-defective module synchronously. UIMaOTO adopts temporary offspring strategy to generate the formal offspring and then proposes the unified integration strategy to enhance the selection pressure of algorithm. UIMaOTO is compared to other state-of-the-art algorithms, and the experiment results are conducted on well-known DTLZ test suite. The results show that the proposed algorithm has better all-around performance and is competitive for many-objective optimization problems. At the same time, the UIMaOTO algorithm is used to address the HD-SDP model, and the performance is improved by 14.27% compared with other algorithms.