An ANN-PSO-based model to predict fault-prone modules in software

Fault‐prone module identification in software helps software developers to allocate effort and resources more efficiently during software testing process. In this paper, the fault‐prone software modules are identified, making use of existing reduced software metrics. Different methods have been used to reduce dimension of software metrics and taken as input of ANN‐based models where the ANN is trained using back propagation algorithm. The back propagation algorithm suffers from local optima problem and, in order to avoid this problem, a global optimisation algorithm such as Particle Swarm Optimisation (PSO) algorithm has been used to train the ANN in this paper. An ANN‐based model trained using PSO (ANN‐PSO) has been proposed in this paper to identify the fault‐prone modules in software. The reduced software metrics from different methods have been taken as input of the proposed ANN‐PSO approach to determine prediction accuracy. A comparative experimental study has been performed on different data sets to show the effectiveness of the proposed ANN‐PSO approach. The experimental results show that the proposed model has better prediction accuracy than the ANN‐based models trained using the conventional back propagation training method.