Software Reliability Growth Model Based on Dynamic Fuzzy Neural Network with Parameters Dynamic Adjustment
Gai‐Ge Wang · 2013
The parameters of dynamic fuzzy neural network were dynamically adjusted by genetic algorithm(GA-DFNN),and GA-DFNN was used to study software reliability growth model(SGRM).The optimal solution of DFNN's parameters was resolved by genetic algorithm in the DFNN's training process,and according to the DFNN which has the optimal parameters,software failure data prediction model was established.According to 3 groups of software defects data,we compared the SGRM's predictive ability established by GA-DFNN with SGRM's predictive ability established by fuzzy neural network(FNN) and BP neural network(BPN).The simulation results confirm that the SRGM established by GA-DFNN has steady short period prediction,and its short period prediction error is small and it has some versatility.