Software reliability modeling under an uncertain testing environment

Haque, Md. Asraful; Ahmad, Nesar

doi:10.1080/02286203.2023.2201905

Cited by 4 publications

(5 citation statements)

References 30 publications

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“…The SPRT algorithm is iterative and requires additional data collection and testing if the probability ratio falls within a certain acceptance region (A and B). Equation (11) expresses the relationship between p 0 and p 1 and the thresholds A and B.…”

Section: Sequential Probability Ratio Testmentioning

confidence: 99%

“…If the value is outside the region, then a conclusion of "Reject" or "Accept" is made. Stieber [28] applied the SPRT to estimate the reliability of NHPP SRGMs, which involved redefining the probability ratios p 0 and p 1 of Equation (11) in terms of the mean value function m(t). Here, p 0 and p 1 are expressed as follows:…”

Section: Sequential Probability Ratio Testmentioning

confidence: 99%

“…The testing environment is also not consistent. Haque et al [11] considered uncertain testing environments such as testing effort, testing skills, and testing coverage. Chatterjee et al [12] studied the randomness effort using uncertain testing environments and operating environments.…”

Section: Introductionmentioning

confidence: 99%

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Study of a New Software Reliability Growth Model under Uncertain Operating Environments and Dependent Failures

Lee,

Chang,

Pham

2023

Mathematics

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The coronavirus disease (COVID-19) outbreak has prompted various industries to embark on digital transformation efforts, with software playing a critical role. Ensuring the reliability of software is of the utmost importance given its widespread use across multiple industries. For example, software has extensive applications in areas such as transportation, aviation, and military systems, where reliability problems can result in personal injuries and significant financial losses. Numerous studies have focused on software reliability. In particular, the software reliability growth model has served as a prominent tool for measuring software reliability. Previous studies have often assumed that the testing environment is representative of the operating environment and that software failures occur independently. However, the testing and operating environments can differ, and software failures can sometimes occur dependently. In this study, we propose a new model that assumes uncertain operating environments and dependent failures. In other words, the model proposed in this study takes into account a wider range of environments. The numerical examples in this study demonstrate that the goodness of fit of the new model is significantly better than that of the existing SRGM. Additionally, we show the utilization of the sequential probability ratio test (SPRT) based on the new model to assess the reliability of the dataset.

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Section: Sequential Probability Ratio Testmentioning

confidence: 99%

Section: Sequential Probability Ratio Testmentioning

confidence: 99%

See 1 more Smart Citation

Study of a New Software Reliability Growth Model under Uncertain Operating Environments and Dependent Failures

Lee,

Chang,

Pham

2023

Mathematics

View full text Add to dashboard Cite

show abstract

“…Because software is used in many different ways, its roles have become very diverse, and the environment provided to each piece of software has also become very diverse. Many studies have been conducted on SRMs that assume uncertainty in operating environments [17][18][19][20][21][22][23][24][25][26]. Here, uncertain operating environments refer to the environments used by actual consumers.…”

Section: Introductionmentioning

confidence: 99%

“…To assume uncertain operating environments, Teng and Pham [18] and Pham [19] proposed an SRM utilizing a gamma distribution, and Song et al [20] assumed the uncertain operating environments to be exponential and applied the failure detection rate function as a monotonically increasing function to present a new model for uncertain operating environments. Many researchers have proposed an SRM that considers uncertain factors [21][22][23][24]. Chang et al [25] proposed an SRM that considers testing coverage, which indicates whether sufficient tests have been performed in uncertain operating environments.…”

Section: Introductionmentioning

confidence: 99%

A Software Reliability Model Considering a Scale Parameter of the Uncertainty and a New Criterion

Song,

Kim,

Pham

et al. 2024

Mathematics

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It is becoming increasingly common for software to operate in various environments. However, even if the software performs well in the test phase, uncertain operating environments may cause new software failures. Traditional proposed software reliability models under uncertain operating environments suffer from the problem of being well-suited to special cases due to the large number of assumptions involved. To improve these problems, this study proposes a new software reliability model that assumes an uncertain operating environment. The new software reliability model is a model that minimizes assumptions and minimizes the number of parameters that make up the model, so that the model can be applied to general situations better than the traditional proposed software reliability models. In addition, various criteria based on the difference between the predicted and estimated values have been used in the past to demonstrate the superiority of the software reliability models. Also, we propose a new multi-criteria decision method that can simultaneously consider multiple goodness-of-fit criteria. The multi-criteria decision method using ranking is useful for comprehensive evaluation because it does not rely on individual criteria alone by ranking and weighting multiple criteria for the model. Based on this, 21 existing models are compared with the proposed model using two datasets, and the proposed model is found to be superior for both datasets using 15 criteria and the multi-criteria decision method using ranking.

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Integrating Burr type testing effort functions in logistic reliability growth model with uncertainty factor

Iqbal,

Manzoor,

Shrivastava

et al. 2023

Int J Syst Assur Eng Manag

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Software reliability modeling under an uncertain testing environment

Cited by 4 publications

References 30 publications

Study of a New Software Reliability Growth Model under Uncertain Operating Environments and Dependent Failures

Study of a New Software Reliability Growth Model under Uncertain Operating Environments and Dependent Failures

A Software Reliability Model Considering a Scale Parameter of the Uncertainty and a New Criterion

Integrating Burr type testing effort functions in logistic reliability growth model with uncertainty factor

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