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

Manjubala Bisi and Neeraj Kumar Goyal

International Journal of Reliability and Safety, 2016, vol. 10, issue 3, 243-264

Abstract: 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.

Keywords: software reliability; reliability prediction; artificial neural networks; ANNs; fault-prone software modules; particle swarm optimisation; PSO; dimension reduction; back propagation; software faults; software development; software testing. (search for similar items in EconPapers)
Date: 2016
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