Multi-release software model based on testing coverage incorporating random effect (SDE)

Bibyan, Ritu; Anand, Sameer; Aggarwal, Anu G.; Kaur, Gurjeet

doi:10.1016/j.mex.2023.102076

Cited by 10 publications

(2 citation statements)

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“…proposed a unification modeling based-SRGM by incorporating the idea of imperfect debugging along with error generation. In the past few years, many researchers have discussed the error generation scenario and imperfect debugging (Anu G Aggarwal, Nijhawan, & Kapur, 2015;Ahmad, Khan, & Rafi, 2010;Anand, Verma, & Aggarwal, 2018;Bibyan, Anand, Aggarwal, & Kaur, 2023;Kapur, Aggarwal, & Garmabaki, 2011;Kapur, Garg, Aggarwal, & Tandon, 2010;Kapur, Gupta, Gupta, & Jha, 2008;Verma, Anand, Kapur, & Aggarwal, 2022;Zhang, Cui, Liu, Meng, & Wu, 2014).…”

Section: Introductionmentioning

confidence: 99%

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Multi-Release Testing coverage-based SRGM considering error generation and change-point incorporating the random effect

Bibyan

Anand

Aggarwal

et al. 2023

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The most essential step during the development of the software is the testing procedure which makes the software dependable and efficient. During this procedure, the observation and rectification of the faults play a significant role in increasing the reliability of the software. Various Software Reliability growth models (SRGMs) with multiple assumptions were presented by various researchers to study the software’s reliability. It is well known that the fault observation rate may get affected by irregular factors causing arbitrary effects. In this study, we aim to capture this irregular variation in fault observation rate by expressing it in terms of testing coverage. The fault observation/removal process has been assumed as a stochastic process and modeled it using an Itô type of stochastic differential equation. Testing coverage enables software designers to check the software’s excellence and to see if any extra efforts are required to enhance reliability. In this paper, we have developed an SRGM based on testing coverage by introducing the concept of changepoint, error generation, and irregular fluctuations. The error generation implies that during the testing procedure faults are not disclosed entirely and more faults get introduced. Later on, we focused on the idea of multi-release by considering four releases. We have estimated the parameters of the model by using fault dataset for consecutive releases of Tandem Computers and validated the performance by evaluating the goodness-of-fit criteria.

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Section: Introductionmentioning

confidence: 99%

“…A generalized SRGM using SDE was modeled by studying simple, hard, and complex faults by 2009). A model was proposed based on a similar concept but the fault detection rate was considered as TC with error generation by Bibyan et al (2023) and a multi-release model was formulated.…”

Section: Introductionmentioning

confidence: 99%