Comparison of software-reliability-growth predictions: neural networks vs parametric-recalibration

Sitte, Renate

doi:10.1109/24.799900

Cited by 91 publications

(33 citation statements)

References 6 publications

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“…The authors considered only average error (AE), average bias (AB), normalized average error (NAE) for predictability measure, but not considered root mean square error (RMSE), normalized root mean square error(NRMSE). Sitte (1999), analyzed two models for software reliability prediction: 1) neural network models and 2) parametric recalibration models. The author used Kolmogorov distance as the common prediction measure in the comparison experiment.…”

Section: Related Workmentioning

confidence: 99%

Software Reliability Assesment using Neural Networks of Computational Intelligence Based on Software Failure Data

Bhuyan¹,

Mohapatra²,

Sethi³

2016

BJMC

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Abstract. The computational intelligence approach using Neural Network (NN) has been known to be very useful in predicting software reliability. Software reliability plays a key role in software quality. In order to improve accuracy and consistency of software reliability prediction, we propose the applicability of Feed Forward Back-Propagation Network (FFBPN) as a model to predict software reliability. The model has been applied on data sets collected across several standard software projects during system testing phase with fault removal. Unlike most connectionist models, our model attempt to compute average error (AE), the root mean square error (RMSE), normalized root mean square error (NRMSE), mean absolute error (MAE) simultaneously. A comparative study among the proposed feed-forward neural network with some traditional parametric software reliability growth model's performance is carried out. The results indicated in this work suggest that FFBPN model exhibit an accurate and consistent behavior in reliability prediction.

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Section: Related Workmentioning

confidence: 99%

Software Reliability Assesment using Neural Networks of Computational Intelligence Based on Software Failure Data

Bhuyan¹,

Mohapatra²,

Sethi³

2016

BJMC

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“…Sitte [8] presented a neural network based method for software reliability prediction. He compared the approach with recalibration for parametric models using some meaningful predictive measures with same datasets.…”

Section: Related Workmentioning

confidence: 99%

Software Reliability Prediction using Neural Network with Encoded Input

Bisi¹,

Goyal²

2012

IJCA

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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. General TermsSoftware reliability prediction, fault count prediction, neural network modeling.

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“…It made the model difficult to use, or lead to the prediction result is not satisfactory. With a detailed study, the neural network prediction method has a better effect than the traditional parametric model prediction [3]. It has good adaptability and can improve the prediction accuracy effectively.…”

Section: Introductionmentioning

confidence: 99%

Rolling Prediction Based Software Reliability Model Consideration with Learning Curve

Tian¹,

Wu²,

Yang³

et al. 2016

JSW

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Software reliability is an important factor for evaluating software quality in the domain of safety-critical software. The neural network prediction method has been widely used in reliability prediction area. However, Data noise and other issues make this approach easy to falling into local optimum, and reduce the accuracy of the prediction, it also affect the applicability of the model. In this paper, we consider the learning curve effect, and proposed a neural network based reliability prediction, utilize the rolling forecast method to elevate the accuracy and applicability of neural network. The method is validated through three groups of public data sets. And the results show a fairly accurate prediction capability.

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Comparison of software-reliability-growth predictions: neural networks vs parametric-recalibration

Cited by 91 publications

References 6 publications

Software Reliability Assesment using Neural Networks of Computational Intelligence Based on Software Failure Data

Software Reliability Assesment using Neural Networks of Computational Intelligence Based on Software Failure Data

Software Reliability Prediction using Neural Network with Encoded Input

Rolling Prediction Based Software Reliability Model Consideration with Learning Curve

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