A Software Reliability Prediction Algorithm Based on MHPSO - BP Neural Network

DOI

10.2991/gcmce-17.2017.10How to use a DOI?

Keywords

Software reliability prediction, Attractor, MHPSO, BP neural network.

Abstract

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.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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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  - Proceedings of the 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  - 10.2991/gcmce-17.2017.10
ID  - Xu2017/06
ER  -