Abstract
The software reliability model is the basis for the quantitative analysis and prediction of software reliability. In recent years, neural networks due to its generalization and learning ability have been widely applied in the field of software reliability modeling. However, the slow convergence and local minimum of neural networks may cause unsuccessful prediction. Therefore, this paper presents a software reliability combination model based on genetic optimization BP neural network. This model uses three classical software reliability models as the input of BP neural network, and then uses the genetic algorithm optimization to automatically configure and optimize the weight and the thresholds. The results of experiments show that the model proposed has better fitting effect and prediction ability than other similar models.
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Wang, R., Jin, F., Yang, L., Han, X. (2018). A Software Reliability Combination Model Based on Genetic Optimization BP Neural Network. In: Yuan, H., Geng, J., Liu, C., Bian, F., Surapunt, T. (eds) Geo-Spatial Knowledge and Intelligence. GSKI 2017. Communications in Computer and Information Science, vol 849. Springer, Singapore. https://doi.org/10.1007/978-981-13-0896-3_15
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DOI: https://doi.org/10.1007/978-981-13-0896-3_15
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