Evolutionary neural network prediction for cumulative failure modeling (original) (raw)
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Evolutionary Prediction for Cumulative Failure Modeling: A Comparative Study
2011 Eighth International Conference on Information Technology: New Generations, 2011
In the past 35 years more than 100 software reliability models are proposed. Most of them are parametric models. In this paper we present a comparative study of different non-parametric models based on the neural networks and regression model learned by the real coded genetic algorithm to predict the cumulative failure in the software. Experimental results show that the training of different models by our real coded genetic algorithm have a good predictive capability across different projects.
Evolutionary regression prediction for software cumulative failure modeling: A comparative study
2009
An evolutionary regression modeling approach for software cumulative failure prediction based on auto-regression order 4, 7 and 10 models are proposed. A real coded genetic algorithm is used to optimize the mean square of the error produced by training the auto-regression model. In this paper, we present a real coded genetic algorithm that uses the appropriate operators for this encoding type to train the autoregression model. To evaluate the predictive capability of the developed model data sets, various projects were used. A comparison between auto-regression order 4 model trained using least square estimation [1] and real coded genetic algorithm training is provided, also a comparison between the autoregression order 7 and 10 models trained using the genetic algorithm is presented. Experimental results show that the training of different auto-regression model by the real coded genetic algorithm has a good predictive capability.
Evolutionary Neural Network Prediction for Software Reliability Modeling
2016
Abstract: Software Reliability is a key concern of many users and developers of softwares. Demand for high software reliability requires robust modeling techniques for software quality prediction. This paper presents a new approach to software reliability assessment by using neural network. The neural network model has been applied to three different applications and normalized root mean of the square of error as an evaluation criterion. Results show that the neural network model adopted has good predictive capability.
Software Reliability Prediction using Neural Network with Encoded Input
A neural network based software reliability model to predict the cumulative number of failures based on Feed Forward architecture is proposed in this paper. Depending upon the available software failure count data, the execution time is encoded using Exponential and Logarithmic function in order to provide the encoded value as the input to the neural network. The effect of encoding and the effect of different encoding parameter on prediction accuracy have been studied. The effect of architecture of the neural network in terms of hidden nodes has also been studied. The performance of the proposed approach has been tested using eighteen software failure data sets. Numerical results show that the proposed approach is giving acceptable results across different software projects. The performance of the approach has been compared with some statistical models and statistical models with change point considering three datasets. The comparison results show that the proposed model has a good prediction capability.
IFIP International Federation for Information Processing
Software failure and software reliability are strongly related concepts. Introducing a model that would perform successful failure prediction could provide the means for achieving higher software reliability and quality. In this context, we have employed artificial neural networks and genetic algorithms to investigate whether software failure can be accurately modeled and forecasted based on empirical data of real systems.
Simulated Annealing Neural Network for Software Failure Prediction
Various models for software reliability prediction were proposed by many researchers. In this work we present a hybrid approach based on the Neural Networks and Simulated Annealing. An adaptive simulated Annealing algorithm is used to optimize the mean square of the error produced by training the Neural Network, predicting software cumulative failure. To evaluate the predictive capability of the proposed approach various projects were used. A comparison between this approach and others is presented. Numerical results show that both the goodness-of-fit and the next-step-predictability of our proposed approach have greater accuracy in predicting software cumulative failure compared with other approaches.
Prediction of software reliability using neural networks
Proceedings. 1991 International Symposium on Software Reliability Engineering, 1991
Software reliability growth models have achieved considerable importance in estimating reliability of software products. This paper explores the use of feed-forward neural networks as a model for software reliability growth prediction. To empirically evaluate the predictive capability of this new approach data sets from di erent software projects are used. The neural networks approach exhibits a consistent behavior in prediction and the predictive performance is comparable to that of parametric models.
Neuro-genetic approach on logistic model based software reliability prediction
Expert Systems with Applications, 2015
In this paper, we propose a multi-layer feedforward artificial neural network (ANN) based logistic growth curve model (LGCM) for software reliability estimation and prediction. We develop the ANN by designing different activation functions for the hidden layer neurons of the network. We explain the ANN from the mathematical viewpoint of logistic growth curve modeling for software reliability. We also propose a neuro-genetic approach for the ANN based LGCM by optimizing the weights of the network using proposed genetic algorithm (GA). We first train the ANN using back-propagation algorithm (BPA) to predict software reliability. After that, we use the proposed GA to train the ANN by globally optimizing the weights of the network. The proposed ANN based LGCM is compared with the traditional Non-homogeneous Poisson process (NHPP) based software reliability growth models (SRGMs) and ANN based software reliability models. We present the comparison between the two training algorithms when they are applied to train the proposed ANN to predict software reliability. The applicability of the different approaches is explained through three real software failure data sets. Experimental results demonstrate that the proposed ANN based LGCM has better fitting and predictive capability than the other NHPP and ANN based software reliability models. It is also noted that when the proposed GA is employed as the learning algorithm to the ANN, the proposed ANN based LGCM gives more fitting and prediction accuracy i.e. the proposed neuro-genetic approach to the LGCM provides utmost predictive validity. Proposed model can be applied during software testing time to get better software reliability estimation and prediction than the other traditional NHPP and ANN based software reliability models.