On Using Soft Computing Techniques in Software Reliability Engineering

Madsen, Henrik; Thyregod, Poul; Burtschy, Bernard; Albeanu, Grigore; Popentiu, Florin

doi:10.1142/s0218539306002094

Cited by 15 publications

(3 citation statements)

References 20 publications

(12 reference statements)

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“…Application of Soft Computing techniques in software reliability engineering has come up recently (Madsen et al, 2006). Despite the recent advancements in the software reliability growth models, it was observed that different models have different predictive capabilities and also no single model is suitable under all circumstances.…”

Section: Literature Surveymentioning

confidence: 99%

Software reliability prediction by soft computing techniques

Kiran

Ravi

2008

Journal of Systems and Software

121

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Section: Literature Surveymentioning

confidence: 99%

Software reliability prediction by soft computing techniques

Kiran

Ravi

2008

Journal of Systems and Software

121

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“…With this development of NHPP as the base, several methodologies have been coming into existence which includes Gompetz [5], S-shaped Distribution [6], [7] and Inversed S-Shaped distribution [8]. Particular models like Jelinski and Moranda model [9], Littlewood & Verrall model [10], Goel -Okumoto model [11], Musa -Okumoto model [12], Kuo, Huang, & Lyu [13], Brown DB [14], Kapur PK [15], Khan, Ahmad and Rafi [16], Ohishi, Okamura and Dohi [17], Satya Prasad, Naga Raju and Kantam [18], Zou and Davis [19], Kiyoshi, Henrik and Poul [20] etc., are also highlighted in the literature with the aim, optimizing the errors in the developed software's. However, these, models are effectual when the errors are estimated most apparently and have a complete overview of discriminating between failure and non-failure.…”

Section: Introductionmentioning

confidence: 99%

A Unique Procedure for Accurate Recognition of Software Failures A Software Reliability Growth Model Based on Special Case of Generalized Gamma Mixture Model

Medapati,

Jasti,

Rajinikanth

2019

IJEAT

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This paper pinpoints to detect and eliminate the actual software failures efficiently. The approach fit in a particular case of Generalized Gamma Mixture Model (GGMM), namely Weibull distribution. The approach estimates two parameters using Maximum Likelihood Estimate (MLE). Standard Evaluation metrics like Mean Square Error (MSE), Coefficient of Determination (R2), Sum of Squares (SSE), and Root Means Square Error (RMSE) were calculated. For the justification of the model selection and goodness of fit various model selection frameworks like Chi-Square Goodness of Fit, Wald’s Test, Akaike Information Criteria (AIC), AICc and Schwarz criterion (SBC) were also estimated. The experimentation was carried out on five benchmark datasets which interpret the considered novel technique identifies the actual failures on par with the existing models. This paper presents a novel software reliability growth model which is more effectual in the identification of the failures significantly and help the present software organizations in the release of software free from bugs just in time

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“…Application of soft‐computing techniques in place of statistical techniques has come up in the recent years in software reliability engineering . Kiran and Ravi proposed software reliability prediction using soft‐computing techniques.…”

Section: Introductionmentioning

confidence: 99%

Neural network for software reliability analysis of dynamically weighted NHPP growth models with imperfect debugging

Rani¹,

Mahapatra²

2018

Software Testing Verif & Rel

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This paper propose a learning algorithm of supervised back-propagation neural networks for dynamic weighted combination of software reliability model. The proposed model is an assimilation of 3 well-known non-homogeneous poisson process (NHPP)-based software reliability growth models with imperfect debugging. The novel approach of proposed supervised back propagation-based neural network 2-stage architecture has a great impact on the network by combining the imperfect debugging models based on the nature of fault introduction rate during testing and debugging. Function approximation metrics are used for comparing the proposed model with individual models. Three data sets are trained using supervised back-propagation neural networks to compare the performance and validity evaluation of proposed and existing NHPP models and dynamic weighted combinational model. Reliability analysis among important NHPP models incorporating imperfect debugging is illustrated through numerical and graphical explanation of several metrics using supervised back-propagation neural networks. KEYWORDS imperfect debugging, fault introduction rate, neural networks, non-homogenous poisson process, software reliability growth model, supervised back-propagation algorithm INTRODUCTIONSoftware plays a vital role in every aspect of technological world with its ever-increasing dependency on technology, and its size and complexity have dramatically increased many folds [1]. Developers are facing a lot of problems to develop a trustworthy and reliable software for the user. A reliable software must meet or exceed the specified usage period, under specified operating conditions in a manner that satisfies customer's expectations.Software reliability is quantitative measurement of quality [2][3][4].Software cannot be developed error-free because of the uncertainties and amount of manual effort involved in the software development process. Specifically, erroneous software incurs high work costs. On the other hand, debugging and testing reduces the error content[5] but increases the development cost [6]. Software failure occurs because of hidden bugs in the software instead of immediate fixes for detected errors during development, but new errors may be introduced during debugging. Some reliability models have also been developed to address fault detection; correction [7, 8]; testing coverage [9]; and imperfect debugging processes [10, 11].Many factors such as software development methodology and complexity of the software are strongly dependant on making assumptions that influences the behaviour of software reliability growth. The software reliability measurement is based on type of data set [1]; therefore, traditional statistical theory plays a vital role in software reliability modelling. There are many constraints on existing statistical models that are based on prior assumptions in software testing. Neural network (NN) approaches can be applied to estimate and predict the software reliability. Neural network methods recommend far better models than ...

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On Using Soft Computing Techniques in Software Reliability Engineering

Cited by 15 publications

References 20 publications

Software reliability prediction by soft computing techniques

Software reliability prediction by soft computing techniques

A Unique Procedure for Accurate Recognition of Software Failures A Software Reliability Growth Model Based on Special Case of Generalized Gamma Mixture Model

Neural network for software reliability analysis of dynamically weighted NHPP growth models with imperfect debugging

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