Hyper-geometric distribution model to estimate the number of residual software faults

Tohma, Y.; Jacoby, R.; Murata, Yuya; Yamamoto, Masaki

doi:10.1109/cmpsac.1989.65155

Cited by 51 publications

(32 citation statements)

References 5 publications

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“…Hence ( | ) R t t is a monotonic increasing function of t . Taking the logarithm on both sides of (10), we obtain ln [ ( ) ( ) ] R m t t m t (27) We can easily determine the testing time needed to reach a desired R by solving (27) and (6). It is noted that () Rt is increasing in t .…”

Section: S/w Release-time Based On Reliability Criterionmentioning

confidence: 99%

“…Suppose the software system desires that the testing would be continued till the operational reliability is equal to 0.80 (at t = 0.1), from (27) and (6), we get t = 28.57 weeks. If the desired reliability is 0.85, then t = 29.90 weeks.…”

Section: Reliability Analysis For Real Data Set Dsmentioning

confidence: 99%

“…The second set of actual data is the pattern of discovery of errors [27]. The debugging time and the number of detected faults per day are reported.…”

Section: Dsmentioning

confidence: 99%

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Software Reliability Growth Modeling with New Modified Weibull Testing–effort and Optimal Release Policy

Quadri¹,

Ahmad²

2010

IJCA

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In software development life cycle, software testing is one of the most important tasks; and in the testing, software reliably is very important aspect for any category of software systems. A number of testing-effort functions for software reliability growth model based on non-homogeneous Poisson process (NHPP) have been proposed in the past. Although these models are quite helpful for software developers and have been widely accepted and applied in the industries and research centers, we still need to put more testing-effort functions into software reliability growth model for accuracy on estimate of the parameters. In this paper, we will consider the case where the time dependent behaviors of testingeffort expenditures are described by New Modified Weibull Distribution (NMWD). Software Reliability Growth Models (SRGM) based on the NHPP are developed which incorporates the (NMWD) testing-effort expenditure during the softwaretesting phase. It is assumed that the error detection rate to the amount of testing-effort spent during the testing phase is proportional to the current error content. Model parameters are estimated by Least Square and Maximum Likelihood estimation techniques, and software measures are investigated through numerical experiments on real data from various software projects. The evaluation results are analyzed and compared with other existing models to show that the proposed SRGM with (NMWD) testing-effort has a fairly better faults prediction capability and it depicts the real-life situation more faithfully. This model can be applied to a wide range of software system. In addition, the optimal release policy for this model, based on reliability criterion is discussed.

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Section: S/w Release-time Based On Reliability Criterionmentioning

confidence: 99%

Section: Reliability Analysis For Real Data Set Dsmentioning

confidence: 99%

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Software Reliability Growth Modeling with New Modified Weibull Testing–effort and Optimal Release Policy

Quadri¹,

Ahmad²

2010

IJCA

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“…For this purpose a real software failure data set has been taken which is published in [26] as first data set. This data set contains 481 cumulative number of faults during 111 days test period.…”

Section: Model Validation and Performance Analysismentioning

confidence: 99%

Effect of Test Coverage and Change Point on Software Reliability Growth Based on Time Variable Fault Detection Probability

Chatterjee¹,

Shukla²

2016

JSW

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Abstract:In past four decades, many software reliability growth models (SRGMs) have been proposed to enhance the reliability of the software system. During the testing process potential fault sites are sensitized to detect the faults. Fault detection probability increases as learning and maturity of the testing personnel increases. Therefore, in this paper a time variant fault detection probability has been introduced and integrated into s-shaped coverage SRGM. Experimental results shows that the proposed model is better and will be helpful to improve the accuracy of the software reliability estimation.

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“…The fourth set of real data is the pattern of discovery of faults by Thoma in [23]. The debugging time and the number of detected faults per day are reported.…”

Section: Numerical Examplementioning

confidence: 99%

Software reliability modeling and cost estimation incorporating testing-effort and efficiency

Huang

Kuo

et al.

Proceedings 10th International Symposium on Software Reliability Engineering (Cat. No.PR00443)

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Hyper-geometric distribution model to estimate the number of residual software faults

Cited by 51 publications

References 5 publications

Software Reliability Growth Modeling with New Modified Weibull Testing–effort and Optimal Release Policy

Software Reliability Growth Modeling with New Modified Weibull Testing–effort and Optimal Release Policy

Effect of Test Coverage and Change Point on Software Reliability Growth Based on Time Variable Fault Detection Probability

Software reliability modeling and cost estimation incorporating testing-effort and efficiency

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