1 edition of **Estimating reliability with discrete growth models** found in the catalog.

Estimating reliability with discrete growth models

James D. Chandler

- 24 Want to read
- 6 Currently reading

Published
**1988**
by Naval Postgraduate School in Monterey, California
.

Written in English

- Operations research

Determining the reliability of newly designed systems is one of the most important functions of the acquisition process in the military. Tracking the growth in reliability of a system as it is developed and modified repeatedly is an important part of the acquisition process. This thesis extends and expands a reliability growth simulation program written previously. It analyzes the capabilities and limitations of two discrete reliability growth models to determine which models are most applicable in estimating system reliability under a variety of different growth patterns. Negative growth patterns are also considered. The result ot this thesis is a FORTRAN simulation which enables a more accurate estimate of system reliability using test data generated during the development phase of an acquisition program. Keywords: Theses; Charts; Mathematical models. (Author)

**Edition Notes**

Statement | James D. Chandler Jr |

Contributions | Naval Postgraduate School (U.S.) |

The Physical Object | |
---|---|

Pagination | xii, 152 p. ; |

Number of Pages | 152 |

ID Numbers | |

Open Library | OL25516071M |

This book is dedicated to the memory of Miss Willie Webb who passed away on April 10 while working at the Center for Risk and Reliability at the University of Maryland (UMD). She initiated the concept of this book, as an aid for students conducting studies in Reliability Engineering at the University of Maryland. UponFile Size: 6MB. 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.

The graphical method. The graphical method is a great way of intuitively grasping discrete-time models with one variable such as the logistic growth model. The first step consists of producing a plot of the function \(f\) defining the recursion equation, e.g. \(f(x)=b(1-x)x\) for the logistic growth model. You can think of the x-axis of this plot as the variable at the current time (\(x_t. systems with reliability goals in the order of 1−10−7 or higher; hence, an alternate approach is neces-sary. The approach generally taken to investigate the reliability of a highly reliable system is 1. Develop a mathematical reliability model of the system 2. Measure or estimate the parameters of the model by:

software reliability important hardware reliability models have also been described in this manuscript. The intent of this manuscript is to present a survey of the available literature on the application of reliability dissertation consists of five chapters with a comprehensive list of references given at the end. In discrete breeding population the species may breed only at a specific time usually at a particular time of the year. Breeding seasons introduce some delay in the regulative process. A population growth model may be defined as continuous population grow.

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Abstract. This paper considers the well known problem of estimating reliability in discrete reliability growth context with sequence of dichotomous success-failure outcomes. More precisely, the authors generalize the simple order relationship constraint with some coefficients.

The authors prove that under some mild conditions, Author: Xiang Shao, Xiangzhong Fang. The discrete reliability growth model applies to well defined trials that result in success or failure. Extending the results of Bhattacharyya, Fries and Johnson () to unequal numbers of trials per equipment configuration, we investigate the large sample properties of some estimators that are analogous to those of the continuous by: 8.

This paper develops a discrete reliability growth (RG) model for an inverse sampling scheme, e.g., for destructive tests of expensive single-shot operations systems where design changes are made only and immediately after the occurrence of by: 6.

Abstract: This paper focuses on discrete reliability-growth models (DRGM), for which the relevant data comprise sequences of dichotomous success-failure outcomes from successive system configurations or stages.

It presents a comprehensive compilation of model descriptions and characterizations, as well as discussions of related statistical methodologies for parameter estimation and confidence Cited by: The discrete software reliability growth models are described with difference equations which have exact solutions.

The models yield accurate parameter estimates in spite of a small amount of. sists of several model equations for estimating: system reliability; the expected number of failure modes observed during testing; the probability of failure due to a new failure mode, and the portion of system un-reliability associated with repeat, or known, failure modes.

These model equations are used to:. Reliability growth models have been applied extensively in analyzing failure data arising from hardware as well as a piece of software.

Further, for systems with single-shot missions that undergo a developmental program, reliability growth models arise quite. We describe software reliability growth models that yield accurate parameter estimates in spite of a small amount of input data in an actual software testing.

These models are based on discrete. Various software reliability growth models have been proposed to predict the reliability of a software. These models help vendors to predict the behaviour of the software before shipment. The reliability is predicted by estimating the parameters of the software reliability growth models.

But the model parameters are generally. Estimating discrete-choice models of product differentiation Steven T. Berry* This article considers the problem of "supply-and-demand" analysis on a cross section of oligopoly markets with differentiated products. The primary methodology is to assume that demand can be described by a discrete-choice model and that prices are endogenously.

Reliability Modeling – The RIAC Guide to Reliability Prediction, Assessment and Estimation The intent of this book is to provide guidance on modeling techniques that can be used to quantify the reliability of a product or system.

In this context, reliability modeling is the process of constructing a mathematical model that is used to estimate. Since the Gompertz curve is a deterministic function, the curve cannot be applied to estimating software reliability which is the probability that software system does not fail in a prefixed time period.

In this article, we propose a stochastic model called the Gompertz software reliability model based on non-homogeneous Poisson by: 1. Estimate the unreliability and reliability by configuration. Solution. The parameter estimates for the Crow-AMSAA model using the parameter estimation for discrete data methodology yields and.

The following table displays the results for probability of failure and reliability, and these results are displayed in. 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. Reliability growth is the improvement in the reliability of a product (component, subsystem or system) over a period of time due to changes in the product's design and/or the manufacturing process.

The concept of reliability growth is not just theoretical or absolute. WHAT IS GROWTH CURVE MODELING. Growth curve modeling is a broad term that has been used in different contexts during the past century to refer to a wide array of statistical models for repeated measures data (see Bollen,and Bollen & Curran,pp.

9–14, for historical reviews).However, within the past decade or so, this term has primarily come to define a discrete set of analytical Cited by: 4. Reliability Growth Models-The exponential model can be regarded as the basic form of software reliability growth model.

•For the past decades, more than a hundred models have been proposed in the research literature. •Unfortunately few have been tested in practical environments with real data, and even fewer are in Size: KB.

ADVERTISEMENTS: There are four procedures in common use for computing the reliability coefficient (sometimes called the self-correlation) of a test. These are: 1. Test-Retest (Repetition) 2. Alternate or Parallel Forms 3. Split-Half Technique 4.

Rational Equivalence. Test-Retest Method: To estimate reliability by means of the test-retest method, the same test is administered twice to [ ]. Software Reliability Models: Time between failures and Accuracy estimation Dalbir Kaur1, Monika Sharma2 M.E Scholar 1 UIET, Supervisor2 UIET2, 1,2Panjab University,Chandigarh, India Abstract—For decide the quality of Software, Software Reliability is a vital and important factor.

These examples appear in the Reliability Growth and Repairable System Analysis Reference book. Parameter Estimation Example 1: Reliability Data. Using the reliability growth data given in the table below, do the following: Find a Gompertz curve that represents the data and plot it with the raw data.

Find a Logistic reliability growth curve that represents the data and plot it with the raw data. Multiple models for measuring the reliability of the software and thus analysts are in a big chaos to decide which model should be used and which one is best.

Thus, this review work depicts the overview and application of the SRGMs. Keywords— Software reliability, software reliability growth model, Residual Errors, Reliability Factor, Time.Figure 2 Random-step function model of reliability growth.

The above models are discrete models that reflect incremental reliability growth. When a new version of the software with repaired faults is delivered for testing it should have a lower rate of failure occurrence than the previous version.

However, to predict the reliability that will.Reliability Growth Planning for Discrete-Use Systems. Abstract: IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must first be obtained from.