A Software Reliability Prediction Algorithm Based on MHPSO - BP Neural Network
Dong Xu, Shaopei Ji, Yulong Meng, Ziying Zhang
Available Online June 2017.
- https://doi.org/10.2991/gcmce-17.2017.10How to use a DOI?
- Software reliability prediction, Attractor, MHPSO, BP neural network.
- Because the weights and thresholds of BP neural network usually adopt random assignment, there is a problem of low accuracy in software reliability prediction. In order to solve this problem, a software reliability prediction algorithm (MHPSO-BP) based on multi-layer heterogeneous PSO optimized BP neural network is proposed in this paper. In this algorithm, the population structure of the particle swarm is set to the hierarchical structure, and the velocity updating equation of the particle is improved by using the attractor. The information interaction between the particles is enhanced, and the optimization performance of the particle swarm optimization algorithm is improved. And then use the improved PSO to optimize the weight and threshold of the BP neural network. The software reliability prediction experiment was performed using the JM1 software defect data set of the NASA-MDP project during the experiment. The results show that the proposed method has better predictive performance than the traditional BP neural network.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Dong Xu AU - Shaopei Ji AU - Yulong Meng AU - Ziying Zhang PY - 2017/06 DA - 2017/06 TI - A Software Reliability Prediction Algorithm Based on MHPSO - BP Neural Network BT - 2017 Global Conference on Mechanics and Civil Engineering (GCMCE 2017) PB - Atlantis Press SP - 47 EP - 53 SN - 2352-5401 UR - https://doi.org/10.2991/gcmce-17.2017.10 DO - https://doi.org/10.2991/gcmce-17.2017.10 ID - Xu2017/06 ER -