@inproceedings{1c77a920dbb1412da0e012cd4fb31de0,
title = "A software reliability combination model based on genetic optimization bp neural network",
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.",
keywords = "BP neural network, Combination model, Genetic algorithm, Software reliability model",
author = "Runan Wang and Fusheng Jin and Li Yang and Xiangyu Han",
note = "Publisher Copyright: {\textcopyright} Springer Nature Singapore Pte Ltd. 2018.; 5th International Conference on Geo-Spatial Knowledge and Intelligence, GSKI 2017 ; Conference date: 08-12-2017 Through 10-12-2017",
year = "2018",
doi = "10.1007/978-981-13-0896-3_15",
language = "English",
isbn = "9789811308956",
series = "Communications in Computer and Information Science",
publisher = "Springer Verlag",
pages = "143--151",
editor = "Hanning Yuan and Jing Geng and Chuanlu Liu and Tisinee Surapunt and Fuling Bian",
booktitle = "Geo-Spatial Knowledge and Intelligence - 5th International Conference, GSKI 2017, Revised Selected Papers",
address = "Germany",
}