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- W2074684359 abstract "Software defect prediction could improve the reliability of software and reduce development costs. Traditional prediction models usually have a lower prediction accuracy. In order to solve this problem, a new model for software defect prediction using Particle Swarm Optimization (PSO) and Support Vector Machine (SVM) named P-SVM model is proposed in this paper, which takes advantage of non-linear computing capability of SVM and parameters optimization capability of PSO. Firstly, P-SVM model uses PSO algorithm to calculate the best parameters of SVM, and then it adopts the optimized SVM model to predict software defect. P-SVM model and other three different prediction models are used to predict the software defects in JM1 data set as an experiment, the results show that P-SVM model has a higher prediction accuracy than BP Neural Network model, SVM model, GA-SVM model." @default.
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- W2074684359 date "2013-05-01" @default.
- W2074684359 modified "2023-10-16" @default.
- W2074684359 title "A new model for software defect prediction using Particle Swarm Optimization and support vector machine" @default.
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- W2074684359 doi "https://doi.org/10.1109/ccdc.2013.6561670" @default.
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