Modelling and analysis of software reliability with Burr type X testing‐effort and release‐time determination

Ahmad, Nesar; Khan, Mohammad G.M.; Quadri, S. M. K.; Kumar, Mahesh

doi:10.1108/17465660910943748

Cited by 27 publications

(17 citation statements)

References 32 publications

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“…During SDLC, project managers have to allocate development and/or testing effort systematically to maximize the software reliability and to minimize potential failure penalties [11]. In the past, there were a lot of papers discussing the relationships between testing-effort (TE) consumption and reliability analysis [12][13][14][15][16][17][18][19][20][21][22][23]. It is also noticed that TE can be typically represented as the number of CPU hours, the number of executed test cases, the amount of consumed manpower, etc.…”

Section: Introductionmentioning

confidence: 99%

Software reliability analysis considering the variation of testing-effort and change-point

Ke¹,

Huang

Peng

2014

Proceedings of the International Workshop on Innovative Software Development Methodologies and Practices

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It is commonly recognized that software development is highly unpredictable and software quality may not be easily enhanced after software product is finished. During the software development life cycle (SDLC), project managers have to solve many technical and management issues, such as high failure rate, cost over-run, low quality, late delivery, etc. Consequently, in order to produce robust and reliable software product(s) on time and within budget, project managers and developers have to appropriately allocate limited development-and testing-effort and time. In the past, the distribution of testing-effort or manpower can typically be described by the Weibull or Rayleigh model. Practically, it should be noticed that development environments or methods could change due to some reasons. Thus when we plan to perform software reliability modeling and prediction, these changes or variations occurring in the development process have to be taken into consideration. In this paper, we will study how to use the Parr-curve model with multiple change-points to depict the consumption of testing-effort and how to perform further software reliability analysis. The applicability and performance of our proposed model will be demonstrated and assessed through real software failure data. Experimental results are analyzed and compared with other existing models to show that our proposed model gives better predictions.

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Section: Introductionmentioning

confidence: 99%

Software reliability analysis considering the variation of testing-effort and change-point

Ke¹,

Huang

Peng

2014

Proceedings of the International Workshop on Innovative Software Development Methodologies and Practices

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“…Testing effort indicates the consumed resources during testing phase and can be measured as human power, the number of test cases, the number of CPU hours and etc [8][9][10]. As a result, several new NHPP SRGMs were proposed by incorporating diverse testing effort functions (TEFs) into the traditional NHPP modeling process, such as the exponential, Rayleigh, Weibull [13], exponentiated Weibull [14], Burr-type X [15], logistic [9], generalized logistic 1 & 2 [16] TEFs and so on.…”

Section: Introductionmentioning

confidence: 99%

Software Reliability Modelling Considering both Testing Effort and Testing Coverage

Liu¹,

Liu²,

Ren³

et al. 2015

Proceedings of the 2015 International Symposium on Computers &Amp; Informatics

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Abstract.Considering testing effort function (TEF) or testing coverage function (TCF) in the non-homogeneous Poisson process (NHPP) software reliability modeling can further improve the fitting and prediction performance of the NHPP software reliability growth models (SRGMs). Thus this paper discusses how to integrate both TEF and TCF into the traditional NHPP software reliability modeling process to capture the integrated effect of testing effort and testing coverage on reliability estimation. First we present a new TEF, i.e. the inflected S-shaped TEF (IS-TEF) for describing the S-shaped varying trend of the testing-effort increasing rate more accurately. Meanwhile, a new NHPP SRGM (named IS-SRGM) which considers the IS-TEF is proposed. Then a comprehensive modeling framework for incorporating the TEF and TCF is proposed. Recur to this framework, a new NHPP SRGM (named IS-LO-SRGM) with both the IS-TEF and logistic TCF (LO-TCF) is proposed. Finally, two case studies for validating the effectiveness of the IS-TEF and the IS-LO-SRGM in terms of fitting results are presented. The results show that: the comprehensive modeling framework to incorporate the TEF and the TCF in NHPP SRGMs achieves a substantial improvement compared with the NHPP SRGMs which only consider the TEF or the TCF.

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“…The equation (31) Furthermore, to commit the provisions of the optimal software release policy for the proposed software reliability as depicted above, a finite and unique real number 1 T is determined such that …”

Section: Minimize C T Subject To R T T T R For C C C T Rmentioning

confidence: 99%

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

Quadri¹,

Ahmad²

2010

IJCA

Self Cite

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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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Modelling and analysis of software reliability with Burr type X testing‐effort and release‐time determination

Cited by 27 publications

References 32 publications

Software reliability analysis considering the variation of testing-effort and change-point

Software reliability analysis considering the variation of testing-effort and change-point

Software Reliability Modelling Considering both Testing Effort and Testing Coverage

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

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