Cognitive Deep Neural Networks prediction method for software fault tendency module based on Bound Particle Swarm Optimization

Identification of module fault tendency is greatly important for cost reduction and software development effectiveness. A DNN (Deep Neural Networks) prediction method for software fault tendency module based on BPSO (Bound Particle Swarm Optimization) dimensionality reduction was proposed in the paper. Firstly, the calculation framework of the DNN prediction algorithm for software fault tendency module based on BPSO dimensionality reduction and 21 software fault measurement indexes as well as the normalization processing method of these index values were provided in the paper; then, the particle swarm optimization algorithm was adopted for the dimensionality reduction of software fault data set, and the particle position was represented by binary (0 or 1) character string to simplify data processing; then, the DNN algorithm was adopted to predict software fault tendency module; finally, the simulation experiments were implemented in four standard test sets-PC1, JM1, KC1 and KC3 to verify the performance advantage of the algorithm.