Testing-Effort Dependent Software Reliability Model for Distributed Systems

Shatnawi, Omar

doi:10.4018/jdst.2013040101

Cited by 6 publications

(2 citation statements)

References 24 publications

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“…Substituting (18) in (15) and solving using the PGF computational technique in terms of the initial condition that at n = 0, m n = 0, we can obtain the closed-form exact solution as 19) and (16) are identical.…”

Section: A Model Developmentmentioning

confidence: 99%

Discrete Time-Space Stochastic Mathematical Modelling for Quantitative Description of Software Imperfect Fault-Debugging with Change-Point

Shatnawi¹,

Shatnawi²

2021

IJACSA

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Statistics and stochastic-process theories, along with the mathematical modelling and the respective empirical evidence support, describe the software fault-debugging phenomenon. In software-reliability engineering literature, stochastic mathematical models based on the non-homogeneous Poisson process (NHPP) are employed to measure and boost reliability too. Since reliability evolves on account of the running of computer test-run, NHPP type of discrete time-space models, or difference-equation, is superior to their continuous time-space counterparts. The majority of these models assume either a constant, monotonically increasing, or decreasing fault-debugging rate under an imperfect fault-debugging environment. However, in the most debugging scenario, a sudden change may occur to the fault-debugging rate due to an addition to, deletion from, or modification of the source code. Thus, the fault-debugging rate may not always be smooth and is subject to change at some point in time called changepoint. Significantly few studies have addressed the problem of change-point in discrete-time modelling approach. The paper examines the combined effects of change-point and imperfect fault-debugging with the learning process on software-reliability growth phenomena based on the NHPP type of discrete timespace modelling approach. The performance of the proposed modelling approach is compared with other existing approaches on an actual software-reliability dataset cited in literature. The findings reveal that incorporating the effect of change-point in software-reliability growth modelling enhances the accuracy of software-reliability assessment because the stochastic characteristics of the software fault-debugging phenomenon alter at the change-point.

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Section: A Model Developmentmentioning

confidence: 99%

Discrete Time-Space Stochastic Mathematical Modelling for Quantitative Description of Software Imperfect Fault-Debugging with Change-Point

Shatnawi¹,

Shatnawi²

2021

IJACSA

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“…A testingeffort function describes the distribution or consumption pattern of testing resources during the testing period. Hence it is very important to track the software reliability growth with respect to the testing-effort expenditure [10][11][12][13][14][15][16][17][18][19][20].…”

Section: Software Reliability Models Based On Nonhomogeneousmentioning

confidence: 99%

Release Policy, Change-Point Concept, and Effort Control through Discrete-Time Imperfect Software Reliability Modelling

Shatnawi¹,

Kapur²,

Shatnawi³

2016

IJCA

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Nonhomogeneous Poisson process based software reliability models play an important role in developing software systems and enhancing the performance of computer software. As software reliability grows on the basis of the execution of computer test runs. Nonhomogeneous Poisson process type of discrete-time software reliability models, or difference equations, is more realistic and often provides better fit than their continuous-time counterparts. Since discrete-time model conserves the properties of the continuous-time model, the estimation of its parameter would be simpler and more accurate. In this paper, we explore the importance of testing resource and imperfect debugging phenomenon consideration in software reliability growth modeling. The resultant model is very useful for the reliability analysis as the measure of reliability is computed considering the distribution of testingeffort, influence of the testing efficiency and the changes of the testing process. Using the resultant model, testing-effort control, change-point concept and optimal release policy have also been investigated. Therefore, this paper thus provides a new insight into development of discrete-time modelling in software reliability engineering, that could be of immense help to the software project manager in monitoring and controlling the testing process closely and effectively allocating the resources in order to reduce the testing cost and to meet the given reliability requirements.

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An Examination of Determinants of Software Testing and Project Management Effort

Subramanian

Pendharkar

Pai

2016

Journal of Computer Information Systems

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Testing-Effort Dependent Software Reliability Model for Distributed Systems

Cited by 6 publications

References 24 publications

Discrete Time-Space Stochastic Mathematical Modelling for Quantitative Description of Software Imperfect Fault-Debugging with Change-Point

Discrete Time-Space Stochastic Mathematical Modelling for Quantitative Description of Software Imperfect Fault-Debugging with Change-Point

Release Policy, Change-Point Concept, and Effort Control through Discrete-Time Imperfect Software Reliability Modelling

An Examination of Determinants of Software Testing and Project Management Effort

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