Statistical methods in software engineering: reliability and risk
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York [u.a.]
Springer
1999
|
| Schriftenreihe: | Springer series in statistics
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XIV, 295 S. Ill., graph. Darst. |
| ISBN: | 0387988238 |
Internformat
MARC
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| 100 | 1 | |a Singpurwalla, Nozer D. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Statistical methods in software engineering |b reliability and risk |c Nozer D. Singpurwalla ; Simon P. Wilson |
| 264 | 1 | |a New York [u.a.] |b Springer |c 1999 | |
| 300 | |a XIV, 295 S. |b Ill., graph. Darst. | ||
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Datensatz im Suchindex
| _version_ | 1804127429354061824 |
|---|---|
| adam_text | Nozer D Singpurwalla
Simon P Wilson
Statistical Methods
in Software Engineering
Reliability and Risk
With 55 Illustrations
Springer
CONTENTS
Preface v
Acknowledgments vii
1 Introduction and Overview l
1 1 What is Software Engineering? 1
1 2 Uncertainty in Software Production 2
121 The Software Development Process 2
122 Sources of Uncertainty in the Development Process 3
1 3 The Quantification of Uncertainty 4
131 Probability as an Approach for
Quantifying Uncertainty 4
132 Interpretations of Probability 6
133 Interpreting Probabilities in Software Engineering 9
1 4 The Role of Statistical Methods in Software Engineering 9
1 5 Chapter Summary 11
2 Foundational Issues: Probability and Reliability 13
2 0 Preamble 13
2 1 The Calculus of Probability 14
211 Notation and Preliminaries 14
212 Conditional Probabilities and Conditional
Independence 16
213 The Calculus of Probability 17
214 The Law of Total Probability, Bayes Law,
and the Likelihood Function 20
215 The Notion of Exchangeability 25
2 2 Probability Models and Their Parameters 28
221 What is a Software Reliability Model? 28
222 Some Commonly Used Probability Models 29
223 Moments of Probability Distributions and
Expectation of Random Variables 39
224 Moments of Probability Models:
The Mean Time to Failure 41
Contents
2 3 Point Processes and Counting Process Models 41
231 The Nonhomogeneous Poisson Process Model 43
232 The Homogeneous Poisson Process Model 45
233 Generalizations of the Point Process Model 46
2 4 Fundamentals of Reliability 52
241 The Notion of a Failure Rate Function 53
242 Some Commonly Used Model Failure Rates 54
243 Covariates in the Failure Rate Function 57
244 The Concatenated Failure Rate Function 58
2 5 Chapter Summary 59
Exercises for Chapter 2 61
3 Models for Measuring Software Reliability 67
3 1 Background: The Failure of Software 67
311 The Software Failure Process and Its
Associated Randomness 68
312 Classification of Software Reliability Models 70
3 2 Models Based on the Concatenated Failure Rate Function 72
321 The Failure Rate of Software 72
322 The Model of Jelinski and Moranda (1972) 72
323 Extensions and Generalizations of the Model
by Jelinski and Moranda 75
324 Hierarchical Bayesian Reliability Growth Models 76
3 3 Models Based on Failure Counts 77
331 Time Dependent Error Detection Models 77
3 4 Models Based on Times Between Failures 80
341 The Random Coefficient Autoregressive
Process Model 80
342A Non-Gaussian Kalman Filter Model 81
3 5 Unification of Software Reliability Models 82
351 Unification via the Bayesian Paradigm 83
352 Unification via Self-Exciting Point Process Models 84
353 Other Approaches to Unification 86
3 6 An Adaptive Concatenated Failure Rate Model 91
361 The Model and Its Motivation 92
Contents xi
362 Properties of the Model and
Interpretation of Model Parameters 94
3 7 Chapter Summary 95
Exercises for Chapter 3 97
4 Statistical Analysis of Software Failure Data 101
4 1 Background: The Role of Failure Data 101
4 2 Bayesian Inference, Predictive Distributions,
and Maximization of Likelihood 103
421 Bayesian Inference and Prediction 104
422 The Method of Maximum Likelihood 105
423 Application: Inference and Prediction Using
Jelinski and Moranda s Model 106
424 Application: Inference and Prediction
Under an Error Detection Model 110
4 3 Specification of Prior Distributions 113
431 Standard of Reference—Noninformative Priors 114
432 Subjective Priors Based on Elicitation
of Specialist Knowledge 115
433 Extensions of the Elicitation Model 117
434 Example: Eliciting Priors for the
Logarithmic-Poisson Model 118
435 Application: Failure Prediction Using
Logarithmic-Poisson Model 120
4 4 Inference and Prediction Using a Hierarchical Model 124
441 Application to NTDS Data:
Assessing Reliability Growth 126
4 5 Inference and Predictions Using Dynamic Models 129
451 Inference for the Random Coefficient
Exchangeable Model 131
452 Inference for the Adaptive Kalman Filter Model 141
453 Inference for the Non-Gaussian
Kalman Filter Model 143
4 6 Prequential Prediction, Bayes Factors,
and Model Comparison 145
xii Contents
461 Prequential Likelihoods and Prequential Prediction 146
462 Bayes Factors and Model Averaging 148
463 Model Complexity—Occam s Razor 150
464 Application: Comparing the Exchangeable,
Adaptive, and Non-Gaussian Models 151
465 An Example of Reversals in the
Prequential Likelihood Ratio 153
4 7 Inference for the Concatenated Failure Rate Model 154
471 Specification of the Prior Distribution 155
472 Calculating Posteriors by Markov Chain
Monte Carlo 157
473 Testing Hypotheses About Reliability
Growth or Decay 159
474 Application to System 40 Data 160
4 8 Chapter Summary 164
Exercises for Chapter 4 166
5 Software Productivity and Process Management 169
5 1 Background: Producing Quality Software 169
52A Growth-Curve Model for Estimating
Software Productivity 170
521 The Statistical Model 171
522 Inference and Prediction Under the
Growth-Curve Model 174
523 Application: Estimating Individual
Software Productivity 176
5 3 The Capability Maturity Model for Process
Management 180
531 The Conceptual Framework 181
532 The Probabilistic Approach for
Hierarchical Classification 183
533 Application: Classifying a Software Developer 186
5 4 Chapter Summary 188
Exercises for Chapter 5 190
Contents xiii
6 The Optimal Testing and Release of Software 191
6 1 Background: Decision Making and the
Calculus of Probability 191
6 2 Decision Making Under Uncertainty 192
6 3 Utility and Choosing the Optimal Decision 194
631 Maximization of Expected Utility 194
632 The Utility of Money 195
6 4 Decision Trees 196
641 Solving Decision Trees 197
6 5 Software Testing Plans 198
6 6 Examples of Optimal Testing Plans 202
661 One-Stage Testing Using the Jelinski-Moranda
Model 202
662 One-and Two-Stage Testing Using the Model
by Goel and Okumoto 206
663 One-Stage Lookahead Testing Using the Model
by Goel and Okumoto 211
664 Fixed-Time Lookahead Testing for the
Goel-Okumoto Model 212
665 One-Bug Lookahead Testing Plans 214
666 Optimality of One-Stage Look Ahead Plans 215
6 7 Application: Testing the NTDS Data 216
6 8 Chapter Summary 217
Exercises for Chapter 6 219
7 Other Developments: Open Problems 221
7 0 Preamble 221
7 1 Dynamic Modeling and the Operational Profile 222
711 Martingales, Predictable Processes, and
Compensators: An Overview 222
712 The Doob-Meyer Decomposition of Counting
Processes 224
713 Incorporating the Operational Profile 227
7 2 Statistical Aspects of Software Testing: Experimental Designs 228
721 Inferential Issues in Random and Partition Testing 229
xiv Contents
722 Comparison of Random and Partition Testing 231
723 Design of Experiments in Software Testing 232
724 Design of Experiments in Multiversion Programming 236
725 Concluding Remarks 237
7 3 The Integration of Module and System Performance 238
731 The Protocols of Control Flow and Data Flow 239
732 The Structure Function of Modularized Software 242
Appendices 247
Appendix A Statistical Computations Using the Gibbs
Sampler 249
A 1 An Overview of the Gibbs Sampler 250
A 2 Generating Random Variates—The Rejection Method 253
A 3 Examples: Using the Gibbs Sampler 254
A31 Gibbs Sampling the Jelinski-Moranda Model 254
A32 Gibbs Sampling the Hierarchical Model 255
A33 Gibbs Sampling the Adaptive Kalman Filter Model 256
A34 Gibbs Sampling the Non-Gaussian Kalman Filter Model 258
Appendix B The Maturity Questionnaire and Responses 261
B 1 The Maturity Questionnaire 261
B 2 Binary (Yes, No) Responses to the Maturity Questionnaire 265
B 3 Prior Probabilities and Likelihoods 266
B31 The Maturity Levels ViMi I Af/ i) 266
B32 The Key Process Areas ViKy) and V(Kij | A/,) 266
B33 The Likelihoods £ ( % g) 268
References 269
Author Index 283
Subject Index 287
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| spelling | Singpurwalla, Nozer D. Verfasser aut Statistical methods in software engineering reliability and risk Nozer D. Singpurwalla ; Simon P. Wilson New York [u.a.] Springer 1999 XIV, 295 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Springer series in statistics Génie logiciel - Méthodes statistiques Software engineering Statistical methods Software Engineering (DE-588)4116521-4 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Software Engineering (DE-588)4116521-4 s Statistik (DE-588)4056995-0 s DE-604 Wilson, Simon P. Verfasser aut HEBIS Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=008677661&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Singpurwalla, Nozer D. Wilson, Simon P. Statistical methods in software engineering reliability and risk Génie logiciel - Méthodes statistiques Software engineering Statistical methods Software Engineering (DE-588)4116521-4 gnd Statistik (DE-588)4056995-0 gnd |
| subject_GND | (DE-588)4116521-4 (DE-588)4056995-0 |
| title | Statistical methods in software engineering reliability and risk |
| title_auth | Statistical methods in software engineering reliability and risk |
| title_exact_search | Statistical methods in software engineering reliability and risk |
| title_full | Statistical methods in software engineering reliability and risk Nozer D. Singpurwalla ; Simon P. Wilson |
| title_fullStr | Statistical methods in software engineering reliability and risk Nozer D. Singpurwalla ; Simon P. Wilson |
| title_full_unstemmed | Statistical methods in software engineering reliability and risk Nozer D. Singpurwalla ; Simon P. Wilson |
| title_short | Statistical methods in software engineering |
| title_sort | statistical methods in software engineering reliability and risk |
| title_sub | reliability and risk |
| topic | Génie logiciel - Méthodes statistiques Software engineering Statistical methods Software Engineering (DE-588)4116521-4 gnd Statistik (DE-588)4056995-0 gnd |
| topic_facet | Génie logiciel - Méthodes statistiques Software engineering Statistical methods Software Engineering Statistik |
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