Optimum release time of a software under periodic debugging schedule

Das, Sudipta; Sengupta, Debasis; Dewanji, Anup

doi:10.1080/03610918.2017.1414251

Cited by 4 publications

(4 citation statements)

References 34 publications

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“…This is true for large commercial software as well. Das et.al( [8])(2017) considered several situations under interval debugging and found optimal testing time for software, though their cost function does not include testing cost exclusively. Dalal and Mallows(2008)( [5]) considered exact confidence on the remaining number of bugs when software testing is completed.…”

Section: Size-biased Concept For Software Reliabilitymentioning

confidence: 99%

“…Several authors ( Dalal and Mallows(1988) [6] , Singpurwalla(1991) [15], Chakraborty and Arthanari(1994) [4], Chakraborty et. al(2019) [1], Das et. al(2017) [8], Vasanthi et.…”

Section: Introductionmentioning

confidence: 99%

“…al(2019) [1], Das et. al(2017) [8], Vasanthi et. al(2013) [16],and many others) have explored different ways of optimizing the time for software release.…”

Section: Introductionmentioning

confidence: 99%

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Optimum Testing Time of Software using Size-Biased Concepts

Chakraborty,

Chatterjee,

Chakraborty

et al. 2019

Preprint

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Optimum software release time problem has been an interesting area of research for several decades now. We introduce here a new concept of size-biased modelling to solve for the optimum software release time. Bayesian approach is used to solve the problem. We also discuss about the applicability of the model for a specific data set, though we believe that the model is applicable to all kind of software reliability data collected in a discrete framework. It has applications in other fields like oil exploration also. Finally, we compare favourably our model with another similar model published recently. We also provide in this article some future possibilities of research work.

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Section: Size-biased Concept For Software Reliabilitymentioning

confidence: 99%

“…Several authors ( Dalal and Mallows(1988) [6] , Singpurwalla(1991) [15], Chakraborty and Arthanari(1994) [4], Chakraborty et. al(2019) [1], Das et. al(2017) [8], Vasanthi et.…”

Section: Introductionmentioning

confidence: 99%

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Optimum Testing Time of Software using Size-Biased Concepts

Chakraborty,

Chatterjee,

Chakraborty

et al. 2019

Preprint

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“…In most cases now, the information logged during software testing becomes test -case-wise and hence discrete in nature. Software reliability studies, particularly, research on optimum duration of software testing under a discrete set up has also been focus of many studies (Chakraborty and Arthanari, 1994;Chakraborty, 1996;Dewanji et al, 2011;Das et al, 2019). Most of the literature available on this tries to develop optimum testing strategy based on the number of remaining bugs in the software (Chakraborty et al, 2019;Eom et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Estimating Software Reliability Using Size-biased Modelling

Dey¹,

Chakraborty²

2022

Preprint

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Software reliability estimation is one of most active area of research in software testing. Since recording of time between failures has often been too difficult to collect, software testing data now have commonly been recorded as test-case-wise in a discrete set up. Although there have many models that were developed to estimate software reliability, they were too restrictive in nature.We have developed a model using the size-biased concept which not only estimates the software reliability, but also estimates the total number of bugs present in the software. The model is highly flexible as it could provide the reliability estimates at the end of software testing and also at any future phase of software testing that could have been conducted in order to improve the software reliability. This flexibility is instrumental to find the stopping phase such that the software reliability achieves a desired optimum level (e.g., 95%). In addition, we also provide a model extension which could be applied on grouped bugs in different regions of a software.We assessed the performance of our model via simulation study and found that each of the key parameters could be estimated with satisfactory level of accuracy. We also applied our model to two different software testing data sets. In the first model application we found that the conducted software testing was inefficient and a considerable amount of further testing is required to achieve a optimum reliability level. On the other hand, the application to second empirical data set has shown that the respective software was highly reliable with software reliability estimate 99.8%.We anticipate that our novel modelling approach to estimate software reliability could be very useful for the users and can potentially be a key tool in the field of software reliability estimation.

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