The Analysis of Faults Detection Software Based on Improved Neural Network Algorithm

Abstract:

The large-scale software is consisted of the components which are quite different. The detection accuracy of the traditional faults detection methods for the large-scale component software is not satisfactory. This paper proposes a large-scale software faults detection methods based on improved neural network combining the features of the large-scale software by computing the stable probability and building the neural network faults detection models. The proposed method can analyze the serial faults of the large-scale software to determine the positions of the faults. The experiment and simulation results show that the improved method for large-scale software fault detection can greatly improve the accuracy.

• Periodical: Applied Mechanics and Materials (Volumes 602-605)
• Pages: 2044-2047
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.602-605.2044
• Online since: August 2014
• Authors: Miao Yan*, Zhi Bao Liu
• Keywords: Component Software, Faults Detection, Improved Neural Network

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