›› 2012, Vol. 42 ›› Issue (05): 1225-1230.

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Software reliability growth model based on SAA-DFNN

LIU Luo1,2, GUO Li-hong1, XIAO Hui1, WANG Jian-jun1, WANG Gai-ge1,2   

  1. 1. Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Science, Changchun 130033, China;
    2. Graduate University of the Chinese Academy of Sciences, Beijing 100039, China
  • Received:2011-11-15 Online:2012-09-01 Published:2012-09-01

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Abstract: Simulated Annealing Algorithm (SAA) is used to dynamically adjust the parameters of Dynamic Fuzzy Neural Network (SAA-DFNN). The SAA-DFNN is applied to study Software Reliability Growth Model (SRGM). The SAA is used to resolve the optimal solution of DFNN parameters in the DFNN software failure data training process. Then according to the obtained DFNN optimal parameters SAA sets up software failure data prediction model. Using three groups of software defect data, the predictive ability of the SRGM established by SAA-DFNN is compared with that of the SRGM established by Fuzzy Neural Network (FNN) and by BP Neural Network (BPN)and G-O model. Simulation results confirm that the SRGM established by SAA-DFNN has steady single-step ahead predictive ability with certain versatility and the prediction error is smaller.

Key words: artificial intelligence, software reliability, dynamic fuzzy neural network, simulated annealing algorithm, single-step ahead prediction

CLC Number: 

  • TP183
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