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

Bhuyan, Manmath Kumar; Mohapatra, Durga Prasad; Sethi, Srinivas

doi:10.22364/bjmc.2016.4.4.26

Cited by 7 publications

(4 citation statements)

References 21 publications

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“…Manmath Kumar et.al, (2016) proposed fuzzy min-max algorithm together with recurrent neural network technique. This model gave good results for prediction capabilities of the developed fuzzy-neural networks model for software reliability [3]. Mohamad Mahdi Askari et.al, (2014) proposed a hybrid method in which, a new learning approach was used in multilayer perceptron neural networks algorithm that increased network efficiency significantly.…”

Section: Related Workmentioning

confidence: 94%

Fault Data Detection in Software Using a Novel FGRNN Algorithm

Rastogi¹,

Rastogi²,

Darbari³

2019

IJCET

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The use and dependence on software in various fields has been the reason why researchers for past decades have spent their efforts on finding better methods to predict software quality and reliability. Soft computing methods have been used to bring efficient improvement in prediction of software reliability. This study proposed a novel method called Fuzzy Greedy Recurrent Neural Network (FGRNN) to assess software reliability by detecting the faults in the software. A deep learning model based on the Recurrent Neural Network (RNN) has been used to predict the number of faults in software. The proposed model consists of four modules. The first module, attribute selection pre-processing, selects the relevant attributes and improves generalization that improves the prediction on unknown data. Second module called, Fuzzy conversion using membership function, smoothly collects the linear sub-models, joined together to provide results. Next, Greedy selection deals with the attribute subset selection problem. Finally, RNN technique is used to predict software failure using previously recorded failure data. To attest the performance of the software, the popular NASA Metric Data Program datasets are used. Experimental results show that the proposed FGRNN model has better performance in reliability prediction compared with existing other parameter based and NN based models.

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Section: Related Workmentioning

confidence: 94%

Fault Data Detection in Software Using a Novel FGRNN Algorithm

Rastogi¹,

Rastogi²,

Darbari³

2019

IJCET

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“…Nevertheless, little effort has been devoted to this area. Sample papers on statistical methods from authentic journals and conferences are reviewed in Table 1 18,26,54–59 …”

Section: Methodsmentioning

confidence: 99%

“…Sample papers on statistical methods from authentic journals and conferences are reviewed in Table 1. 18,26,[54][55][56][57][58][59] 3.1.2 SRGM using Bayesian methods to increase the accuracy of prediction…”

Section: Srgm Modelsmentioning

confidence: 99%

Software reliability prediction: A survey

Oveisi

Moeini

Mirzaei

et al. 2022

Quality & Reliability Eng

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Softwares play an important role in controlling complex systems. Monitoring the proper functioning of the components of such systems is the principal role of softwares. Often, a petite fault in one of the subsystems may cause irreparable damages; therefore, it is of great importance to be able to predict software faults and estimate the reliability of softwares. In this survey, we present a classification of various methods proposed in the literature to predict software reliability. This study summarizes the results of more than 200 research papers in the field. We also discuss the challenges involved in prediction methods along with proposed partial solutions (i.e., Bayesian methods) to improve the accuracy of such predictions. Moreover, we review numerous evaluation measures introduced so far to assess the performance of prediction models, the datasets they are based on, and also the results they yield.

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“…The insights received from outcomes of simulation are important in designing strategies for optimal operation of the system. Manmath Kumar Bhuyan ,et al, [9] presented a novel technique for software reliability estimation utilizing feed forward neural network with back-propagation. Most of the predictive criteria are considered.…”

Section: Introductionmentioning

confidence: 99%

Estimate and Analysis the Availability of Generator in Electric Power Plant Using ANN

Awad

Ahmed

Abdulateef

2022

alkej

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The large number of failure in electrical power plant leads to the sudden stopping of work. In some cases, the necessary reserve materials are not available for maintenance which leads to interrupt of power generation in the electrical power plant unit. The present study, deals with the determination of availability aspects of generator in unit 5 of Al-Dourra electric power plant. In order to evaluate this generator's availability performance, a wide range of studies have been conducted to gather accurate information at the level of detail considered suitable to achieve the availability analysis aim. The Weibull Distribution is used to perform the reliability analysis via Minitab 17, and Artificial Neural Networks (ANNs) by approaching of Feed-Forward, Back-Propagation. Operating data from the years 2015–2017 were used to calculate the availability by traditional method (Weibull distribution) and train the ANNs, while data from the year 2018 of operation were used to verify the model. The study implies that the ANN may be able to forecast the availability of the generator with a correlation coefficient (R) 0.99874 and a Mean Square Error (MSE) 5.6937E-06 between the availability predicted by ANN and Weibull distribution output.

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

Cited by 7 publications

References 21 publications

Fault Data Detection in Software Using a Novel FGRNN Algorithm

Fault Data Detection in Software Using a Novel FGRNN Algorithm

Software reliability prediction: A survey

Estimate and Analysis the Availability of Generator in Electric Power Plant Using ANN

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