Software reliability prediction model based on ICA algorithm and MLP neural network

Noekhah, Shirin; Hozhabri, Ali Akbar; Rizi, Hamideh Salimian

doi:10.1109/ecdc.2013.6556733

Cited by 10 publications

(9 citation statements)

References 15 publications

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“…That is why many researchers have focused on non‐PSRGMs. These models generally use different machine learning approaches such as neural networks and support vector machine . Neural network models have better robustness, but the convergence of the training process is slow and can easily fall into local minima, which cannot guarantee best solution.…”

Section: Related Workmentioning

confidence: 99%

An improved software reliability prediction model by using high precision error iterative analysis method

Jabeen

Luo

Afzal

2019

Software Testing Verif & Rel

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Software reliability deals with the probability that software will not cause the failure of a system in a specified time interval. Software reliability growth models (SRGMs) are used to predict future behaviour from known characteristics of software, like historical failures. With the increasing demand to deliver quality software, more accurate SRGMs are required to estimate the software release time and cost of the testing effort. Software failure predictions at early phases also provide an opportunity for investing in proper quality assurance and upfront resource planning. Up till now, many parametric software reliability growth models (PSRGMs) have been proposed. However, several limitations of them mean that their predictive capacities differ from one dataset to others. In this paper, to enhance the prediction accuracy of existing PSRGMs, a high precision error iterative analysis method (HPEIAM) has been proposed based on the residual errors. In HPEIAM, residual errors from the estimated results of SRGMs are considered as another source of data that can combine the residual error modification with artificial neural network sign estimator. The repeated computation of residual errors by SRGMs improves and corrects the prediction accuracy up to the expected level. The performance of HPEIAM is tested with several PSRGMs using two sets of real software failure data based on three performance criteria. Moreover, we have compared the estimated failures predicted by HPEIAM with genetic algorithm (GA)-based prediction improvement. The results demonstrate that HPEIAM gives an improvement in goodness-of-fit and predictive performance for every PSRGM in initial few iterations.

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Section: Related Workmentioning

confidence: 99%

An improved software reliability prediction model by using high precision error iterative analysis method

Jabeen

Luo

Afzal

2019

Software Testing Verif & Rel

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“…The paper [11] for the traditional reliability evaluation model in the efficiency and accuracy of the problem, put forward the multilevel fast learning machine learning model based on reliability, effectively enhance the reliability evaluation model itself. In [12], it uses genetic iterative algorithm to solve the problem of how to set up the weight coefficients of each factor in software reliability evaluation model.…”

Section: Related Workmentioning

confidence: 99%

Reliability Evaluation Model of Embedded System Based on Machine Learning Method

Ge¹,

Xu²,

Li³

2018

dtcse

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Embedded system is widely used in all walks of life, and its reliability has become the focus of attention. According to the embedded system reliability evaluation problem, we propose a reliability evaluation model based on machine learning, classification and analysis of the model based on the gradient descent method in machine learning, adaptability of the model itself, which has a strong versatility. The experimental results show that this method has higher accuracy rate and average accuracy of 91.15%. It can provide good support for the reliability evaluation of embedded system. 1

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“…In the related works published latter (see, e.g. Hu et al, 2007;Mahajana et al, 2015;Noekhah et al, 2013), more sophisticated neural networks are used to make a one-stage look ahead prediction with the software fault-detection time data. It should be pointed out that the neural network approach has some drawbacks in application to software fault prediction.…”

Section: Introductionmentioning

confidence: 99%

Optimal stopping time of software system test via artificial neural network with fault count data

Begum

2018

JQME

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Purpose The purpose of this paper is to present a novel method to estimate the optimal software testing time which minimizes the relevant expected software cost via a refined neural network approach with the grouped data, where the multi-stage look ahead prediction is carried out with a simple three-layer perceptron neural network with multiple outputs. Design/methodology/approach To analyze the software fault count data which follows a Poisson process with unknown mean value function, the authors transform the underlying Poisson count data to the Gaussian data by means of one of three data transformation methods, and predict the cost-optimal software testing time via a neural network. Findings In numerical examples with two actual software fault count data, the authors compare the neural network approach with the common non-homogeneous Poisson process-based software reliability growth models. It is shown that the proposed method could provide a more accurate and more flexible decision making than the common stochastic modeling approach. Originality/value It is shown that the neural network approach can be used to predict the optimal software testing time more accurately.

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Software reliability prediction model based on ICA algorithm and MLP neural network

Cited by 10 publications

References 15 publications

An improved software reliability prediction model by using high precision error iterative analysis method

An improved software reliability prediction model by using high precision error iterative analysis method

Reliability Evaluation Model of Embedded System Based on Machine Learning Method

Optimal stopping time of software system test via artificial neural network with fault count data

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