Limited Failure Censored Life Test Sampling Plan in Burr Type X Distribution

Kantam, R. R. L.; Ravikumar, Muppirala

doi:10.22237/jmasm/1478003100

Cited by 3 publications

(3 citation statements)

References 22 publications

(32 reference statements)

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“…Research is also carried out not only using the Non-Homogeneous Poisson Processes but other distribution methods like a family of Pareto distribution R. Satya Prasad [9] [10], R.L. Kantham et.al, [11], where the authors have develpoed new methods to estimate the failure rates and identify the mean time to failure. Latest studies also include works based on Raleigh distribution, Generalized Laplacian distribution, Weibull distribution, and Gaussian distribution.…”

Section: Background Researchmentioning

confidence: 99%

An Empirical Software Reliability Growth Model for Identification of True Failures

Medapati¹,

Jasti²,

TV³

2019

IJITEE

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Software Reliability is a special topic of software engineering that deals with the finding of glitches during the software development. Effective analysis of the reliability helps to understand the quality of the software. It also helps to reveal the number of failures occurred in development phase which facilitates refinement of the failures in the developed software’s. If the failures are not minimized the number of reviews in the software development process increases which in turn increase the expenditure to develop the software. Every software organization aims at releasing the software in time and also it becomes a mandate to manage the software such that the time to release the software is optimized. It becomes a mandate for any organization to release software patches so as to minimize the errors after software release and thereby if the number of patches increases, the credibility of the software together with the storage area will be at stake. This article presents a novel case study wherein a procedural layout is presented such that the number of failures can be reduced instantaneously and the failures are identified at the early stage. The development procedure laid in this article helps to formulate a basis for the distinction between true failures and non-failures. The work is presented using benchmark datasets and the results showcase a better recognition rate and failure deduction rate.

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Section: Background Researchmentioning

confidence: 99%

An Empirical Software Reliability Growth Model for Identification of True Failures

Medapati¹,

Jasti²,

TV³

2019

IJITEE

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“…A new direction for estimating the reliability was proposed by [23], where the authors have developed a model namely Gomptez distribution and this methodology is proven to be a most validating method for estimation of the failures. Research is also extended not only using the Non-Homogeneous Poisson Processes but other distributions like a family of Pareto distribution was carried out by [24], [25] and [26], where the authors have formulated new ideologies for estimating the failure rates and identify the mean time to failure. Latest studies were also published where most of the works are based on Weibull distribution, generalized Laplacian distribution, Raleigh distribution and Gaussian distribution.…”

Section: Background Studymentioning

confidence: 99%

A Novel Assessment to Achieve Maximum Efficiency in Optimizing Software Failures

Medapati¹,

J²,

Rajinikanth³

2019

ijacsa

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Software Reliability is a specialized area of software engineering which deals with the identification of failures while developing the software. Effective analysis of the reliability helps to signify the number of failures occurred during the development phase. This in turn aid in the refinement of the failures occurred during the development of software. This paper identifies a novel assessment to detect and eliminate the actual software failures efficiently. The approach fits in an exponential log normal distribution of Generalized Gamma Mixture Model (GGMM). The approach estimates two parameters using the Maximum Likelihood Estimate (MLE). Standard Evaluation metrics like Mean Square Error (MSE), Coefficient of Determination (R2), Sum of Squares (SSE), and Root Means Square Error (RMSE) were calculated. The experimentation was carried out on five benchmark datasets which interpret the considered novel technique identifies the actual failures on par with the existing models. This novel software reliability growth model which is more effectual in the identification of the failures significantly and facilitate the present software organizations in the release of software free from bugs just in time.

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“…Wu and Tsai (2000) [3], [4], Jun et al (2006) [5] have proposed LFCLTSP on Weibull distribution, with respective distinct approaches in working out the parameters of the sampling plan. Kantam and Ravi Kumar (2016) [6] developed LFCLTSP for Burr Type X distribution. Subba Rao et al (2016) [7] worked on Economic Reliability Sampling Plan with PR distribution.…”

Section: Introductionmentioning

confidence: 99%

Limited Failure Censored Life Test Sampling Plan: Pareto-Rayleigh Model

Prasad¹,

Rao²,

Kantam³

2018

IJET

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A combination of Pareto and Rayleigh distributions considered for a new T-X probability model named as Pareto-Rayleigh distribution (PR distribution). We have chosen a life time random variable X from PR distribution whose lots are to be decided for acceptance or otherwise based on sample lifetimes drawn from the lot. A sampling scheme is developed in such way that the sample is divided into different groups and experiment is terminated whenever the first failure noticed in each group. The theory of ordered statistics is applied for the criterion of acceptance and is compared with the similar works proposed by other authors.

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Limited Failure Censored Life Test Sampling Plan in Burr Type X Distribution

Cited by 3 publications

References 22 publications

An Empirical Software Reliability Growth Model for Identification of True Failures

An Empirical Software Reliability Growth Model for Identification of True Failures

A Novel Assessment to Achieve Maximum Efficiency in Optimizing Software Failures

Limited Failure Censored Life Test Sampling Plan: Pareto-Rayleigh Model

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