A software reliability growth model for an error-removal phenomenon

Kapur, P. K.; Garg, Renu

doi:10.1049/sej.1992.0030

Cited by 169 publications

(87 citation statements)

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“…Then, 3 and 4 values are estimated for mean value function ( ) = 4 (1− (− 3 ( ( ))) ) using software failure data pair ( , ) and here is the estimated values of ( ). The activation functions for hidden layers are ln(1+ ) and 1− (− ) .…”

Section: Parameter Estimation Using Artificial Neural Networkmentioning

confidence: 99%

“…Many such SRGMs have been proposed as parametric [1][2][3][4][5][6][7][8][9][10][11][12][13][14] and nonparametric [15][16][17][18] models since the year 1972 to estimate future failure occurrence times and assess the reliability growth of software systems during the testing phase. The traditional SRGMs are based on the premise that the mean value function of the model follows either exponential growth [1,3] or S-shaped growth [2,11] or both [4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Approach for Software Reliability Growth Modeling with Infinite Testing Effort Function

Ramasamy

Lakshmanan

2017

Mathematical Problems in Engineering

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Reliability is one of the quantifiable software quality attributes. Software Reliability Growth Models (SRGMs) are used to assess the reliability achieved at different times of testing. Traditional time-based SRGMs may not be accurate enough in all situations where test effort varies with time. To overcome this lacuna, test effort was used instead of time in SRGMs. In the past, finite test effort functions were proposed, which may not be realistic as, at infinite testing time, test effort will be infinite. Hence in this paper, we propose an infinite test effort function in conjunction with a classical Nonhomogeneous Poisson Process (NHPP) model. We use Artificial Neural Network (ANN) for training the proposed model with software failure data. Here it is possible to get a large set of weights for the same model to describe the past failure data equally well. We use machine learning approach to select the appropriate set of weights for the model which will describe both the past and the future data well. We compare the performance of the proposed model with existing model using practical software failure data sets. The proposed log-power TEF based SRGM describes all types of failure data equally well and also improves the accuracy of parameter estimation more than existing TEF and can be used for software release time determination as well.

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Section: Parameter Estimation Using Artificial Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine Learning Approach for Software Reliability Growth Modeling with Infinite Testing Effort Function

Ramasamy

Lakshmanan

2017

Mathematical Problems in Engineering

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“…SRGMs base their predictions on data from the testing process and, thus, reflect the testing, the fault prologue and the fault pronouncement processes. Kapur et al [2,3] proposed an SRGM with three types of fault. For each type, the FRR per remaining faults is assumed to be time independent.…”

Section: Nhpp Based Software Reliability Growth Modelsmentioning

confidence: 99%

Review on Software Reliability Growth Models and Software Release Planning

Upadhyay¹,

Johri²

2013

IJCA

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In this paper, Software Reliability Engineering is a field that developed from ancestry in the reliability disciplines of structural, electrical, and hardware engineering. Reliability models are powerful tools of Software Reliability Engineering for estimating, predicting, devious, and assessing software reliability. On the basis of the review the cataloging of software reliability models has been presented as a major part. This categorization is based on the various dimensions of reliability models. Models under review reflect either infinite or finite number of failures. This paper discusses a twodimensional software reliability growth modeling framework. We measured that an actual software reliability growth progression depends not only on testing time but also on testing effort and also enables us to portray software release planning problem in software reliability growth process. Thus, we can say that software project managers can demeanor more viable and accurate software reliability appraisal by using two-dimensional SRGM. General TermsSRGM(Software Reliability Growth Model), Software Release Planning.

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“…[4], [6], [16]. One of the recent trend seen in the field of software industry is to provide upgraded versions of the software with some added functionalities.…”

Section: Introductionmentioning

confidence: 99%

Multi Up-gradation Software Reliability Growth Model Considering the Joint Effect of Testing and Operational Phase

Verma¹,

Parihar²,

Das³

2017

IJCA

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Software companies are coming with multiple add-ons to survive in the purely competitive environment. Each succeeding up-gradation offers some performance enhancement and distinguishes itself from the past release making it more prone to failures. For developing highly reliable software it is important to understand the manner in which faults might encounter. Majority of researchers have focused on understanding the fault removal phenomenon during testing phase but few have also focused on operational phase. With the aim of catering more realistic scenario for comprehending the fault removal process for successive release, both the faults of new release along with remaining bugs of its preceding release has been considered; wherein it is assumed that remaining faults of previous release can be debugged in its operational phase together with testing phase of newer version. Convolution of probability distribution function has been considered for capturing the effect of faults removed in testing (new release) and operational phase of just previous release. Further, two different cases are formulated depending upon the failure distribution being followed for testing as well as for operational phase. The proposed cases are validated on real data set.

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A software reliability growth model for an error-removal phenomenon

Cited by 169 publications

References 1 publication

Machine Learning Approach for Software Reliability Growth Modeling with Infinite Testing Effort Function

Machine Learning Approach for Software Reliability Growth Modeling with Infinite Testing Effort Function

Review on Software Reliability Growth Models and Software Release Planning

Multi Up-gradation Software Reliability Growth Model Considering the Joint Effect of Testing and Operational Phase

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