+612 9045 4394
Statistical Methods in Software Engineering : Reliability and Risk - Nozer D. Singpurwalla

Statistical Methods in Software Engineering

Reliability and Risk

Hardcover Published: 5th August 1999
ISBN: 9780387988238
Number Of Pages: 297

Share This Book:


RRP $464.99
or 4 easy payments of $80.47 with Learn more
Ships in 7 to 10 business days

Other Available Editions (Hide)

  • Paperback View Product Published: 27th September 2012

In establishing a framework for dealing with uncertainties in software engineering, and for using quantitative measures in related decision-making, this text puts into perspective the large body of work having statistical content that is relevant to software engineering. Aimed at computer scientists, software engineers, and reliability analysts who have some exposure to probability and statistics, the content is pitched at a level appropriate for research workers in software reliability, and for graduate level courses in applied statistics computer science, operations research, and software engineering.

Prefacep. v
Acknowledgmentsp. vii
Introduction and Overviewp. 1
What is Software Engineering?p. 1
Uncertainty in Software Productionp. 2
The Software Development Processp. 2
Sources of Uncertainty in the Development Processp. 3
The Quantification of Uncertaintyp. 4
Probability as an Approach for Quantifying Uncertaintyp. 4
Interpretations of Probabilityp. 6
Interpreting Probabilities in Software Engineeringp. 9
The Role of Statistical Methods in Software Engineeringp. 9
Chapter Summaryp. 11
Foundational Issues: Probability and Reliabilityp. 13
Preamblep. 13
The Calculus of Probabilityp. 14
Notation and Preliminariesp. 14
Conditional Probabilities and Conditional Independencep. 16
The Calculus of Probabilityp. 17
The Law of Total Probability, Bayes' Law, and the Likelihood Functionp. 20
The Notion of Exchangeabilityp. 25
Probability Models and Their Parametersp. 28
What is a Software Reliability Model?p. 28
Some Commonly Used Probability Modelsp. 29
Moments of Probability Distributions and Expectation of Random Variablesp. 39
Moments of Probability Models: The Mean Time to Failurep. 41
Point Processes and Counting Process Modelsp. 41
The Nonhomogeneous Poisson Process Modelp. 43
The Homogeneous Poisson Process Modelp. 45
Generalizations of the Point Process Modelp. 46
Fundamentals of Reliabilityp. 52
The Notion of a Failure Rate Functionp. 53
Some Commonly Used Model Failure Ratesp. 54
Covariates in the Failure Rate Functionp. 57
The Concatenated Failure Rate Functionp. 58
Chapter Summaryp. 59
Exercises for Chapter 2p. 61
Models for Measuring Software Reliabilityp. 67
Background: The Failure of Softwarep. 67
The Software Failure Process and Its Associated Randomnessp. 68
Classification of Software Reliability Modelsp. 70
Models Based on the Concatenated Failure Rate Functionp. 72
The Failure Rate of Softwarep. 72
The Model of Jelinski and Moranda (1972)p. 72
Extensions and Generalizations of the Model by Jelinski and Morandap. 75
Hierarchical Bayesian Reliability Growth Modelsp. 76
Models Based on Failure Countsp. 77
Time Dependent Error Detection Modelsp. 77
Models Based on Times Between Failuresp. 80
The Random Coefficient Autoregressive Process Modelp. 80
A Non-Gaussian Kalman Filter Modelp. 81
Unification of Software Reliability Modelsp. 82
Unification via the Bayesian Paradigmp. 83
Unification via Self-Exciting Point Process Modelsp. 84
Other Approaches to Unificationp. 86
An Adaptive Concatenated Failure Rate Modelp. 91
The Model and Its Motivationp. 92
Properties of the Model and Interpretation of Model Parametersp. 94
Chapter Summaryp. 95
Exercises for Chapter 3p. 97
Statistical Analysis of Software Failure Datap. 101
Background: The Role of Failure Datap. 101
Bayesian Inference, Predictive Distributions, and Maximization of Likelihoodp. 103
Bayesian Inference and Predictionp. 104
The Method of Maximum Likelihoodp. 105
Application: Inference and Prediction Using Jelinski and Moranda's Modelp. 106
Application: Inference and Prediction Under an Error Detection Modelp. 110
Specification of Prior Distributionsp. 113
Standard of Reference--Noninformative Priorsp. 114
Subjective Priors Based on Elicitation of Specialist Knowledgep. 115
Extensions of the Elicitation Modelp. 117
Example: Eliciting Priors for the Logarithmic-Poisson Modelp. 118
Application: Failure Prediction Using Logarithmic-Poisson Modelp. 120
Inference and Prediction Using a Hierarchical Modelp. 124
Application to NTDS Data: Assessing Reliability Growthp. 126
Inference and Predictions Using Dynamic Modelsp. 129
Inference for the Random Coefficient Exchangeable Modelp. 131
Inference for the Adaptive Kalman Filter Modelp. 141
Inference for the Non-Gaussian Kalman Filter Modelp. 143
Prequential Prediction, Bayes Factors, and Model Comparisonp. 145
Prequential Likelihoods and Prequential Predictionp. 146
Bayes' Factors and Model Averagingp. 148
Model Complexity--Occam's Razorp. 150
Application: Comparing the Exchangeable, Adaptive, and Non-Gaussian Modelsp. 151
An Example of Reversals in the Prequential Likelihood Ratiop. 153
Inference for the Concatenated Failure Rate Modelp. 154
Specification of the Prior Distributionp. 155
Calculating Posteriors by Markov Chain Monte Carlop. 157
Testing Hypotheses About Reliability Growth or Decayp. 159
Application to System 40 Datap. 160
Chapter Summaryp. 164
Exercises for Chapter 4p. 166
Software Productivity and Process Managementp. 169
Background: Producing Quality Softwarep. 169
A Growth-Curve Model for Estimating Software Productivityp. 170
The Statistical Modelp. 171
Inference and Prediction Under the Growth-Curve Modelp. 174
Application: Estimating Individual Software Productivityp. 176
The Capability Maturity Model for Process Managementp. 180
The Conceptual Frameworkp. 181
The Probabilistic Approach for Hierarchical Classificationp. 183
Application: Classifying a Software Developerp. 186
Chapter Summaryp. 188
Exercises for Chapter 5p. 190
The Optimal Testing and Release of Softwarep. 191
Background: Decision Making and the Calculus of Probabilityp. 191
Decision Making Under Uncertaintyp. 192
Utility and Choosing the Optimal Decisionp. 194
Maximization of Expected Utilityp. 194
The Utility of Moneyp. 195
Decision Treesp. 196
Solving Decision Treesp. 197
Software Testing Plansp. 198
Examples of Optimal Testing Plansp. 202
One-Stage Testing Using the Jelinski-Moranda Modelp. 202
One-and Two-Stage Testing Using the Model by Goel and Okumotop. 206
One-Stage Lookahead Testing Using the Model by Goel and Okumotop. 211
Fixed-Time Lookahead Testing for the Goel-Okumoto Modelp. 212
One-Bug Lookahead Testing Plansp. 214
Optimality of One-Stage Look Ahead Plansp. 215
Application: Testing the NTDS Datap. 216
Chapter Summaryp. 217
Exercises for Chapter 6p. 219
Other Developments: Open Problemsp. 221
Preamblep. 221
Dynamic Modeling and the Operational Profilep. 222
Martingales, Predictable Processes, and Compensators: An Overviewp. 222
The Doob-Meyer Decomposition of Counting Processesp. 224
Incorporating the Operational Profilep. 227
Statistical Aspects of Software Testing: Experimental Designsp. 228
Inferential Issues in Random and Partition Testingp. 229
Comparison of Random and Partition Testingp. 231
Design of Experiments in Software Testingp. 232
Design of Experiments in Multiversion Programmingp. 236
Concluding Remarksp. 237
The Integration of Module and System Performancep. 238
The Protocols of Control Flow and Data Flowp. 239
The Structure Function of Modularized Softwarep. 242
Appendicesp. 247
Statistical Computations Using the Gibbs Samplerp. 249
An Overview of the Gibbs Samplerp. 250
Generating Random Variates--The Rejection Methodp. 253
Examples: Using the Gibbs Samplerp. 254
Gibbs Sampling the Jelinski-Moranda Modelp. 254
Gibbs Sampling the Hierarchical Modelp. 255
Gibbs Sampling the Adaptive Kalman Filter Modelp. 256
Gibbs Sampling the Non-Gaussian Kalman Filter Modelp. 258
The Maturity Questionnaire and Responsesp. 261
The Maturity Questionnairep. 261
Binary (Yes, No) Responses to the Maturity Questionnairep. 265
Prior Probabilities and Likelihoodsp. 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
Referencesp. 269
Author Indexp. 283
Subject Indexp. 287
Table of Contents provided by Syndetics. All Rights Reserved.

ISBN: 9780387988238
ISBN-10: 0387988238
Series: Springer Series in Statistics
Audience: Professional
Format: Hardcover
Language: English
Number Of Pages: 297
Published: 5th August 1999
Publisher: Springer-Verlag New York Inc.
Country of Publication: US
Dimensions (cm): 23.5 x 15.5  x 1.27
Weight (kg): 0.54