Title: Prediction of software inter-failure times using artificial neural network and particle swarm optimisation models
Authors: Manjubala Bisi; Neeraj Kumar Goyal
Addresses: Reliability Engineering Centre, Indian Institute of Technology (IIT) Kharagpur, West Bengal, India ' Reliability Engineering Centre, Indian Institute of Technology (IIT) Kharagpur, West Bengal, India
Abstract: Knowledge of time between failures is required for achieving certain reliability goal in a given amount of testing to determine release time of a software. In practice, existing artificial neural network (ANN) models to predict time between failures utilise complex and variable architecture. In this paper, an ANN model and an ANN-PSO model with fixed and simple architecture are proposed to predict time between failures. Effects of scaling time between failures data have been analysed using logarithmic function. In ANN model, the scaling parameter of logarithmic function is determined by varying maximum scaled value in a certain range. In ANN-PSO model, the scaling parameter is determined automatically during ANN training using particle swarm optimisation (PSO) algorithm. Proposed models are applied on literature reported datasets and found that ANN-PSO model provides better performance than ANN model trained using back propagation algorithm in terms of normalised mean square error.
Keywords: software reliability prediction; artificial neural networks; ANNs; input data scaling; output data scaling; software testing; time between failures prediction; time series ANN modelling; particle swarm optimisation; PSO; software testing.
International Journal of Software Engineering, Technology and Applications, 2015 Vol.1 No.2/3/4, pp.222 - 244
Available online: 30 Mar 2016 *Full-text access for editors Access for subscribers Purchase this article Comment on this article