Software running defect recognition method based on brainstorming optimisation algorithm

In order to improve the accuracy of software running defect recognition, this paper proposes a new method of software running defect recognition based on brainstorming optimisation algorithm. Firstly, K nearest neighbour algorithm is used to cluster and sample software operation data, and Tomek chain removal algorithm is used to remove duplicate data from software operation data. Secondly, after extracting the optimal values of scale factor, displacement factor and inter layer connection weight with brainstorming optimisation algorithm, wavelet neural network is trained to complete the optimisation of wavelet neural network. Finally, the software operation data after de duplication is input into the optimised wavelet neural network, and the output result is the defect identification result. The experimental results show that the number of software program defects identified by this method is 0 in a specified time, and it has the ability to accurately identify software running defects.