Statistical Methods in Software Engineering

Reliability and Risk

By: Nozer D. Singpurwalla, Simon P. Wilson

Write A Review

Hardcover | 5 August 1999

At a Glance

Hardcover
316 Pages

Dimensions(cm)
23.39 x 15.6 x 1.91

Hardcover

$249.75

or 4 interest-free payments of $62.44 with

Ships in 5 to 7 business days

This preface pertains to three issues that we would like to bring to the attention of the readers: our objectives, our intended audience, and the nature of the material. We have in mind several objectives. The first is to establish a framework for dealing with uncertainties in software engineering, and for using quantitative measures for decision making in this context. The second is to bring into perspective the large body of work having statistical content that is relevant to software engineering, which may not have appeared in the traditional outlets devoted to it. Connected with this second objective is a desire to streamline and organize our own thinking and work in this area. Our third objective is to provide a platform that facilitates an interface between computer scientists and statisticians to address a class of problems in computer science. It appears that such an interface is necessary to provide the needed synergism for solving some difficult problems that the subject poses. Our final objective is to serve as an agent for stimulating more cross-disciplinary research in computer science and statistics. To what extent the material here will meet our objectives can only be assessed with the passage of time. Our intended audience is computer scientists, software engineers, and reliability analysts, who have some exposure to probability and statistics. Applied statisticians interested in reliability problems are also a segment of our intended audience.

Shipping

	Standard Shipping	Express Shipping
Metro postcodes:	$9.99	$14.95
Regional postcodes:	$9.99	$14.95
Rural postcodes:	$9.99	$14.95

Orders over $79.00 qualify for free shipping.

How to return your order

At Booktopia, we offer hassle-free returns in accordance with our returns policy. If you wish to return an item, please get in touch with Booktopia Customer Care.

Additional postage charges may be applicable.

Defective items

If there is a problem with any of the items received for your order then the Booktopia Customer Care team is ready to assist you.

For more info please visit our Help Centre.

You Can Find This Book In

Non-Fiction Computing & I.T.Computer Programming & Software Development Software Engineering Mathematics Probability & Statistics Applied Mathematics Computer Science

Other Editions and Formats

Paperback

Published: 27th September 2012

View Product

Preface	p. v
Acknowledgments	p. vii
Introduction and Overview	p. 1
What is Software Engineering?	p. 1
Uncertainty in Software Production	p. 2
The Software Development Process	p. 2
Sources of Uncertainty in the Development Process	p. 3
The Quantification of Uncertainty	p. 4
Probability as an Approach for Quantifying Uncertainty	p. 4
Interpretations of Probability	p. 6
Interpreting Probabilities in Software Engineering	p. 9
The Role of Statistical Methods in Software Engineering	p. 9
Chapter Summary	p. 11
Foundational Issues: Probability and Reliability	p. 13
Preamble	p. 13
The Calculus of Probability	p. 14
Notation and Preliminaries	p. 14
Conditional Probabilities and Conditional Independence	p. 16
The Calculus of Probability	p. 17
The Law of Total Probability, Bayes' Law, and the Likelihood Function	p. 20
The Notion of Exchangeability	p. 25
Probability Models and Their Parameters	p. 28
What is a Software Reliability Model?	p. 28
Some Commonly Used Probability Models	p. 29
Moments of Probability Distributions and Expectation of Random Variables	p. 39
Moments of Probability Models: The Mean Time to Failure	p. 41
Point Processes and Counting Process Models	p. 41
The Nonhomogeneous Poisson Process Model	p. 43
The Homogeneous Poisson Process Model	p. 45
Generalizations of the Point Process Model	p. 46
Fundamentals of Reliability	p. 52
The Notion of a Failure Rate Function	p. 53
Some Commonly Used Model Failure Rates	p. 54
Covariates in the Failure Rate Function	p. 57
The Concatenated Failure Rate Function	p. 58
Chapter Summary	p. 59
Exercises for Chapter 2	p. 61
Models for Measuring Software Reliability	p. 67
Background: The Failure of Software	p. 67
The Software Failure Process and Its Associated Randomness	p. 68
Classification of Software Reliability Models	p. 70
Models Based on the Concatenated Failure Rate Function	p. 72
The Failure Rate of Software	p. 72
The Model of Jelinski and Moranda (1972)	p. 72
Extensions and Generalizations of the Model by Jelinski and Moranda	p. 75
Hierarchical Bayesian Reliability Growth Models	p. 76
Models Based on Failure Counts	p. 77
Time Dependent Error Detection Models	p. 77
Models Based on Times Between Failures	p. 80
The Random Coefficient Autoregressive Process Model	p. 80
A Non-Gaussian Kalman Filter Model	p. 81
Unification of Software Reliability Models	p. 82
Unification via the Bayesian Paradigm	p. 83
Unification via Self-Exciting Point Process Models	p. 84
Other Approaches to Unification	p. 86
An Adaptive Concatenated Failure Rate Model	p. 91
The Model and Its Motivation	p. 92
Properties of the Model and Interpretation of Model Parameters	p. 94
Chapter Summary	p. 95
Exercises for Chapter 3	p. 97
Statistical Analysis of Software Failure Data	p. 101
Background: The Role of Failure Data	p. 101
Bayesian Inference, Predictive Distributions, and Maximization of Likelihood	p. 103
Bayesian Inference and Prediction	p. 104
The Method of Maximum Likelihood	p. 105
Application: Inference and Prediction Using Jelinski and Moranda's Model	p. 106
Application: Inference and Prediction Under an Error Detection Model	p. 110
Specification of Prior Distributions	p. 113
Standard of Reference--Noninformative Priors	p. 114
Subjective Priors Based on Elicitation of Specialist Knowledge	p. 115
Extensions of the Elicitation Model	p. 117
Example: Eliciting Priors for the Logarithmic-Poisson Model	p. 118
Application: Failure Prediction Using Logarithmic-Poisson Model	p. 120
Inference and Prediction Using a Hierarchical Model	p. 124
Application to NTDS Data: Assessing Reliability Growth	p. 126
Inference and Predictions Using Dynamic Models	p. 129
Inference for the Random Coefficient Exchangeable Model	p. 131
Inference for the Adaptive Kalman Filter Model	p. 141
Inference for the Non-Gaussian Kalman Filter Model	p. 143
Prequential Prediction, Bayes Factors, and Model Comparison	p. 145
Prequential Likelihoods and Prequential Prediction	p. 146
Bayes' Factors and Model Averaging	p. 148
Model Complexity--Occam's Razor	p. 150
Application: Comparing the Exchangeable, Adaptive, and Non-Gaussian Models	p. 151
An Example of Reversals in the Prequential Likelihood Ratio	p. 153
Inference for the Concatenated Failure Rate Model	p. 154
Specification of the Prior Distribution	p. 155
Calculating Posteriors by Markov Chain Monte Carlo	p. 157
Testing Hypotheses About Reliability Growth or Decay	p. 159
Application to System 40 Data	p. 160
Chapter Summary	p. 164
Exercises for Chapter 4	p. 166
Software Productivity and Process Management	p. 169
Background: Producing Quality Software	p. 169
A Growth-Curve Model for Estimating Software Productivity	p. 170
The Statistical Model	p. 171
Inference and Prediction Under the Growth-Curve Model	p. 174
Application: Estimating Individual Software Productivity	p. 176
The Capability Maturity Model for Process Management	p. 180
The Conceptual Framework	p. 181
The Probabilistic Approach for Hierarchical Classification	p. 183
Application: Classifying a Software Developer	p. 186
Chapter Summary	p. 188
Exercises for Chapter 5	p. 190
The Optimal Testing and Release of Software	p. 191
Background: Decision Making and the Calculus of Probability	p. 191
Decision Making Under Uncertainty	p. 192
Utility and Choosing the Optimal Decision	p. 194
Maximization of Expected Utility	p. 194
The Utility of Money	p. 195
Decision Trees	p. 196
Solving Decision Trees	p. 197
Software Testing Plans	p. 198
Examples of Optimal Testing Plans	p. 202
One-Stage Testing Using the Jelinski-Moranda Model	p. 202
One-and Two-Stage Testing Using the Model by Goel and Okumoto	p. 206
One-Stage Lookahead Testing Using the Model by Goel and Okumoto	p. 211
Fixed-Time Lookahead Testing for the Goel-Okumoto Model	p. 212
One-Bug Lookahead Testing Plans	p. 214
Optimality of One-Stage Look Ahead Plans	p. 215
Application: Testing the NTDS Data	p. 216
Chapter Summary	p. 217
Exercises for Chapter 6	p. 219
Other Developments: Open Problems	p. 221
Preamble	p. 221
Dynamic Modeling and the Operational Profile	p. 222
Martingales, Predictable Processes, and Compensators: An Overview	p. 222
The Doob-Meyer Decomposition of Counting Processes	p. 224
Incorporating the Operational Profile	p. 227
Statistical Aspects of Software Testing: Experimental Designs	p. 228
Inferential Issues in Random and Partition Testing	p. 229
Comparison of Random and Partition Testing	p. 231
Design of Experiments in Software Testing	p. 232
Design of Experiments in Multiversion Programming	p. 236
Concluding Remarks	p. 237
The Integration of Module and System Performance	p. 238
The Protocols of Control Flow and Data Flow	p. 239
The Structure Function of Modularized Software	p. 242
Appendices	p. 247
Statistical Computations Using the Gibbs Sampler	p. 249
An Overview of the Gibbs Sampler	p. 250
Generating Random Variates--The Rejection Method	p. 253
Examples: Using the Gibbs Sampler	p. 254
Gibbs Sampling the Jelinski-Moranda Model	p. 254
Gibbs Sampling the Hierarchical Model	p. 255
Gibbs Sampling the Adaptive Kalman Filter Model	p. 256
Gibbs Sampling the Non-Gaussian Kalman Filter Model	p. 258
The Maturity Questionnaire and Responses	p. 261
The Maturity Questionnaire	p. 261
Binary (Yes, No) Responses to the Maturity Questionnaire	p. 265
Prior Probabilities and Likelihoods	p. 266
The Maturity Levels P(M[subscript i] M[subscript i-1])	p. 266
The Key Process Areas P(K[subscript ij]) and P(K[subscript ij] M[subscript i])	p. 266
The Likelihoods L(K[subscript ij]; R[superscript ij])	p. 268
References	p. 269
Author Index	p. 283
Subject Index	p. 287
Table of Contents provided by Syndetics. All Rights Reserved.

Statistical Methods in Software Engineering

Reliability and Risk

At a Glance

Hardcover

Shipping

How to return your order

Defective items

You Can Find This Book In

Other Editions and Formats

Paperback

More in Software Engineering

Designing Data-Intensive Applications

Big Ideas Behind Reliable, Scalable, and Maintainable Systems

Radio Frequency and Microwave Design for Next Generation Wireless Applications

Smart Technologies for Engineers and Scientists

Code

2nd Edition - The Hidden Language of Computer Hardware and Software

The Pragmatic Programmer 2nd edition

your journey to mastery, 20th Anniversary Edition

The Essence of Software Engineering

Semantics With Applications

Advanced Topics in Computer Science

Fundamentals of Software Architecture

A Modern Engineering Approach

Site Reliability Engineering

How Google Runs Production Systems

Head First Design Patterns

Building Extensible and Maintainable Object-Oriented Software 2nd Edition

The Manager`s Path

A Guide for Tech Leaders Navigating Growth and Change

Building Microservices

Designing Fine-Grained Systems 2nd Edition

Making Embedded Systems

2nd Edition - Design Patterns for Great Software

Design Patterns

Elements of Reusable Object-Oriented Software

The Art of Spyro

Reignited Trilogy

The Staff Engineer's Path

A Guide For Individual Contributors Navigating Growth and Change

Architecture Patterns with Python

Enabling Test-Driven Development, Domain-Driven Design, and Event-Driven Microservices

Git : Pocket Guide

A Working Introduction

Developing Graphics Frameworks with Java and OpenGL

Software Architecture: The Hard Parts

Modern Trade-Off Analyses for Distributed Architectures

Swim Training Patterns

1st Edition - Plan your Training Sessions with the Power of Mathematics

Test-Driven Development with Python

Obey the Testing Goat: Using Django, Selenium, and JavaScript

Hacking For Dummies

For Dummies (Computer/Tech)

JavaScript : The Good Parts

Unearthing the excellence in JavaScript

User Story Mapping

Discover the Whote Story, Build the Right Product

This product is categorised by