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|Statement||Douglas R. Miller, Ariela Sofer.|
|Series||NASA-CR -- 186438., NASA contractor report -- NASA CR-186438.|
|Contributions||Sofer, Ariela., United States. National Aeronautics and Space Administration.|
|The Physical Object|
Download A nonparametric software reliability growth model
A nonparametric software reliability growth model. September ; Source; NTRS; Authors: software reliability growth model can estimate the cost of the optimal release time and testing.
The authors (Proc. Eighth Int. Conf. Software Eng., London, England, p, ) previously introduced a nonparametric model for software-reliability growth which is based on complete.
Get this from a library. A nonparametric software reliability growth model. [Douglas R Miller; Ariela Sofer; United States.
National Aeronautics and Space Administration.]. I IEEE TRANSACTIONS ON RELIABILITY, VOL, NO. 3, AUGUST A Nonparametric Software-Reliability Growth Model Ariela Sofer George Mason Douglas R.
Miller George Mason University, Fairfax. A nonparametric-Bayes reliability-growth model Abstract: The authors propose a nonparametric reliability-growth model based on Bayes analysis techniques. By using the unique properties of the assumed prior distributions, the moments of the posterior distribution of the failure rate at various stages during a development test can be by: Software reliability growth models are a statistical interpolation ofdefect detection data by mathematical functions.
The functions are used to predict future failure rates orthe number ofresidual defects in the code. There are different ways to represent defect detection data as discussed in Section Sofer, Ariela, and Douglas R.
Miller. "A nonparametric software-reliability growth model." Reliability, IEEE Transactions on (): Google Scholar Cross Ref; Karunanithi, Nachimuthu, Darrell Whitley, and Yashwant K.
Malaiya. "Prediction of software reliability. A reliability growth model is a model of how the system reliability changes over time during the testing process. As system failures are discovered, the underlying faults causing these failures are repaired so that the reliability of the system should improve during system testing and debugging.
A practical method for the estimation of software reliability growth in the early stage of testing. In: International Symposium on Software Reliability (ISSRE), ; p– Google Scholar. Abstract: Reliable softwares are the need of modern digital era.
Failure nonlinearity makes software reliability a complicated task. Over past decades, many researchers have contributed many parametric / non parametric software reliability growth models. In this article, we improve a non-parametric order statistics-based software reliability model by Barghout, Littlewood and Abdel-Ghaly (), from the standpoints of estimation algorithm and.
This book is divided into eight sections and begins with a chapter on adaptive modeling used to predict software reliability, followed by a discussion on failure rate in software reliability growth models. The next chapter deals with methods for predicting and estimating software reliability, with emphasis on their strengths and weaknesses.
Basic software reliability concepts and definitions are discussed. A scheme for classifying software reliability models is presented. A set of criteria for comparing models that is generally accepted by workers in the field is described.
Results of some general comparisons of groups of models are provided. These plots are commonly known as software reliability growth curves and they are usually increasing and strictly concave, or increasing and S-shaped in appearance.
Fig. 1 in Section 4 depicts an increasing and concave reliability growth curve, using data taken from Tohma et al. The data are the results of real tests performed on a large.
The simplest model that illustrates the concept of reliability growth is a step function model (Jelinski and Moranda, ). The reliability increases by a constant increment each time a fault (or a set of faults) is discovered and repaired (Figure 1) and a new version of the software is created.
The Extended Reliability Growth Projection Model for test-fix-find-test was developed by Crow and presented at the Reliability and Maintainability Symposium (RAMS) to address the common and practical case where some corrective actions are incorporated during test and some corrective actions are delayed and incorporated at the end of the test.
By utilizing the technical knowledge about a program, a test, and test data, we can select an appropriate software reliability analysis model for accurate quality assessment. The delayed S-shaped growth model, the inflection S-shaped model, and the hyperexponential model are proposed.
Publisher Summary. This chapter examines stochastic treatment of the failure rate in software reliability growth models.
The problem of software reliability prediction has gained increasing importance since the beginning of the last decade and many statistical models are now available to users.
Software reliability growth models can be classified into two major classes, depending on the dependent variable of the model. For the time between failures models, the variable under study is the time between failures. This is the earliest class of models proposed for software reliability assessment.
Abstract. In this article, we improve a non-parametric order statistics-based software reliability model by Barghout, Littlewood and Abdel-Ghaly (), from the standpoints of estimation algorithm and reliability measure. Software reliability is an important factor for quantitatively characterizing software quality and estimating the duration of software testing period.
Traditional parametric software reliability growth models (SRGMs) such as nonhomogeneous Poisson process (NHPP) models have been successfully utilized in practical software reliability engineering.
: Reliability Growth: Enhancing Defense System Reliability (): National Research Council, Division of Behavioral and Social Sciences and Education, Committee on National Statistics, Panel on Reliability Growth Methods for Defense Systems: Books.
Applied Nonparametric Statistics in Reliability (Springer Series in Reliability Engineering) - Kindle edition by Gámiz, M. Luz, Kulasekera, K. B., Limnios, Nikolaos, Lindqvist, Bo Henry. Download it once and read it on your Kindle device, PC, phones or tablets.
Use features like bookmarks, note taking and highlighting while reading Applied Nonparametric Statistics in Reliability Manufacturer: Springer. middle and later stages of developmental testing.
In contrast, except for when the entire system is software, it is appropriate for software reliability growth to be primarily considered as a component-level concern, which would be addressed while the system is in development by the contractor, or at the latest, during the earliest stages of developmental testing.
The comparison between the parametric method and non-parametric method shows that the deviation in reliability values is less.
Calculation of MTTF under actual conditions using acceleration model. From the parametric and non-parametric method, the MTTF of capacitors under accelerated conditions is found to be hours. The Software Reliability Growth Model (SRGM) can be used to predict the number of failures that may be encountered during the software testing process.
In this ﬁed as either parametric or non-parametric models. The most famous parametric models are the Non-Homogeneous Poisson Process (NHPP) models used in –.
Non-parametric. Nonparametric statistics has probably become the leading methodology for researchers performing data analysis. It is nevertheless true that, whereas these methods have already proved highly effective in other applied areas of knowledge such as biostatistics or social sciences, nonparametric analyses in reliability currently form an interesting area of study that has not yet been fully explored.
Software Reliability Growth Models (SRGMs) are used as indicators of the number of failures that may be faced after the shipping of the software and thus are indicators of the readiness of the. This paper proposes a new nonparametric reliability growth model for the analysis of the failure rate of a system that is undergoing development test.
The only restrictions on the actual, unknown failure distribution for each stage of testing is that it be continuous, have only one unknown parameter??, and have an associated unimodal likelihood function. Software reliability growth model (SRGM) - log-power testing effort function (LPTEF) The Method do not recommend extended with new shiny SRGM, but the proposal extends the excellent work done by analysts in the past, and using the work of several power test run for the model timing.
The following non-parametric analysis methods are essentially variations of this concept. Kaplan-Meier Estimator. The Kaplan-Meier estimator, also known as the product limit estimator, can be used to calculate values for non-parametric reliability for data sets with multiple failures and suspensions.
The equation of the estimator is given by. Software reliability growth models (SRGMs) based on a nonhomogeneous Poisson process (NHPP) are widely used to describe the stochastic failure behavior and assess the reliability of software systems.
For these models, the testing-effort effect and the fault interdependency play significant roles. Considering a power-law function of testing effort and the interdependency of multigeneration.
A variety of Software Reliability Growth Models (SRGM) have been presented in literature. These models suffer many problems when handling various types of project. The reason is; the nature of each project makes it difficult to build a model which can generalize.
In this paper we propose the use of Genetic Programming (GP) as an eVolutionary computation approach to handle the software. Software Engineering | Reliability Growth Models Last Updated: The reliability growth group of models measures and predicts the improvement of reliability programs through the testing process.
The growth model represents the reliability or failure rate of a system as a function of time or the number of test cases. Nonparametric growth curve Estimate growth curves of the mean cost of maintaining the system or the mean number of repairs over time without making assumptions about the distribution of the cost or number of Minitab, choose Stat > Reliability/Survival > Repairable System Analysis > Nonparametric Growth Curve.
Regression with life data. During operation of the software, any data about its failure is stored in statistical form and is given as input to the reliability growth model.
Using this data, the reliability growth model can evaluate the reliability of software. Much data about reliability growth model is available with probability models claiming to represent failure process.
Overview of Software Reliability Growth (Estimation) Models Software reliability growth (or estimation) models use failure data from testing to forecast the failure rate or MTBF into the future.
The models depend on the assumptions about the fault rate during testing which can either be increasing, peaking, decreasing or some combination of. Version 15 JMP, A Business Unit of SAS SAS Campus Drive Cary, NC “The real voyage of discovery consists not in seeking new landscapes, but in having new eyes.”.
Search the information of the editorial board members by name. A Bootstrapping Approach for Software Reliability Measurement Based on a Discretized NHPP Model.
Shinji Inoue, Shigeru Yamada. Journal of Software Engineering and Applications Vol.6 No.4A，Ap DOI: /jseaA 5, Downloads 7, Views Citations This article belongs to the Special Issue on Software. Software reliability is a key part in software quality.
The study of software reliability can be categorized into three parts: modeling, measurement and improvement. Software reliability modeling has matured to the point that meaningful results can be. A nonparametric software reliability growth model Miller and Sofer have presented a nonparametric method for estimating the failure rate of a software program.
The method is based on the complete monotonicity property of the failure rate function, and uses a regression approach to obtain estimates of the current software failure rate.
When it comes to Reliability and Maintainability (R&M), the public and private sectors’ objectives appear to be aligned. The theme for the annual R&M Symposium (RAMS) — a conference focused on the latest technical practices and procedures presented through technical papers and tutorials — was, “R&M in a Model-Based Systems Engineering Environment.”.Reliability Growth, formerly known as RGA software, is an advanced module application available in ReliaSoft Weibull++ that allows you to apply reliability growth models to analyze data from both developmental testing and fielded repairable systems.