Abstract
The novel software defect prediction model based on GA-BP algorithm was proposed in the paper considering the disadvantage of traditional BP (abbreviated for Back Propagation) neural network, which has the problem of easy to fall into local optimization when constructing software defect prediction model, and finally affects the prediction accuracy. Firstly, the optimization ability of GA (abbreviated for Genetic Algorithms) is introduced to optimize the weights and thresholds of Back Propagation neural network. Then the prediction model was constructed based on the GA-BP. Meanwhile the public dataset MDP from NASA was selected and the tool WEKA was used to clean the data and format conversion and as the result, four datasets is available. In the end, experimental results show that the proposed method in the paper is effective for software defect prediction.
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Acknowledgements
This work was supported by Sichuan Science and Technology Programs (Grant No. 2019YJ0252), the Fundamental Research Funds for the Central Universities, SWUN (Grant No. 2019YYXS04) and Key laboratory of Computer System, State Ethnic Affairs Commission, Southwest Minzu University.
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Cui, M., Huang, Y., Luo, J. (2019). Software Defect Prediction Model Based on GA-BP Algorithm. In: Vaidya, J., Zhang, X., Li, J. (eds) Cyberspace Safety and Security. CSS 2019. Lecture Notes in Computer Science(), vol 11983. Springer, Cham. https://doi.org/10.1007/978-3-030-37352-8_13
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DOI: https://doi.org/10.1007/978-3-030-37352-8_13
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