Software metrics: a rigorous and practical approach
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boca Raton, Fla. [u.a.]
CRC Press
2015
|
| Ausgabe: | 3. ed. |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Previous ed.: Boston, Mass.; London: PWS, 1996 Includes bibliographical references and index |
| Beschreibung: | XXI, 595 S. Ill., graph. Darst. |
| ISBN: | 9781439838228 1439838224 |
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| 245 | 1 | 0 | |a Software metrics |b a rigorous and practical approach |c Norman E. Fenton ; James Bieman |
| 250 | |a 3. ed. | ||
| 264 | 1 | |a Boca Raton, Fla. [u.a.] |b CRC Press |c 2015 | |
| 300 | |a XXI, 595 S. |b Ill., graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 500 | |a Previous ed.: Boston, Mass.; London: PWS, 1996 | ||
| 500 | |a Includes bibliographical references and index | ||
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| 650 | 4 | |a Software measurement / Problems, exercises, etc | |
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Datensatz im Suchindex
| _version_ | 1804151557146542080 |
|---|---|
| adam_text | Titel: Software metrics
Autor: Fenton, Norman E
Jahr: 2015
Contents
Preface, xvii
Acknowledgments, xix
Authors, xxi
Part I Fundamentals of Measurement and Experimentation
Chapter 1 . Measurement: What Is It and Why Do It? 3
1.1 MEASUREMENT IN EVERYDAY LIFE 4
1.1.1 What Is Measurement? 4
1.1.2 What Is Not Measurable Make Measurable 7
1.2 MEASUREMENT IN SOFTWARE ENGINEERING 11
1.2.1 Neglect of Measurement in Software Engineering 12
1.2.2 Objectives for Software Measurement 14
1.2.2.1 Managers 14
1.2.2.2 Developers 15
1.2.3 Measurement for Understanding, Control, and
Improvement 16
1.3 SCOPE OF SOFTWARE METRICS 17
1.3.1 Cost and Effort Estimation 18
1.3.2 Data Collection 18
1.3.3 Quality Models and Measures 19
1.3.4 Reliability Models 19
1.3.5 Security Metrics 20
1.3.6 Structural and Complexity Metrics 20
1.3.7 Capability Maturity Assessment 20
vi ¦ Contents
1.3.8 Management by Metrics 21
1.3.9 Evaluation of Methods and Tools 21
1.4 SUMMARY 22
EXERCISES 22
Chapter 2 . The Basics of Measurement 25
2.1 THE REPRESENTATIONAL THEORY OF MEASUREMENT 26
2.1.1 Empirical Relations 27
2.1.2 The Rules of the Mapping 32
2.1.3 The Representation Condition of Measurement 33
2.2 MEASUREMENT AND MODELS 40
2.2.1 Defining Attributes 42
2.2.2 Direct and Derived Measurement 44
2.2.3 Measurement for Prediction 47
2.3 MEASUREMENT SCALES AND SCALE TYPES 51
2.3.1 Nominal Scale Type 53
2.3.2 Ordinal Scale Type 54
2.3.3 Interval Scale Type 56
2.3.4 Ratio Scale Type 58
2.3.5 Absolute Scale Type 59
2.4 MEANINGFULNESS IN MEASUREMENT 61
2.4.1 Statistical Operations on Measures 65
2.4.2 Objective and Subjective Measures 68
2.4.3 Measurement in Extended Number Systems 70
2.4.4 Derived Measurement and Meaningfulness 75
2.5 SUMMARY 78
EXERCISES 79
REFERENCES 84
FURTHER READING 85
Chapter 3 ¦ A Goal-Based Framework for Software
Measurement 87
3.1 CLASSIFYING SOFTWARE MEASURES 87
Contents ¦ vii
3.1.1 Processes 91
3.1.2 Products 92
3.1.2.1 External Product Attributes 93
3.1.2.2 Internal Product Attributes 93
3.1.2.3 The Importance of Internal Attributes 95
3.1.2.4 Internal Attributes and Quality Control
and Assurance 96
3.1.2.5 Validating Composite Measures 96
3.1.3 Resources 98
3.1.4 Change and Evolution 99
3.2 DETERMINING WHAT TO MEASURE 100
3.2.1 Goal-Question-Metric Paradigm 100
3.2.2 Measurement for Process Improvement 105
3.2.3 Combining GQM with Process Maturity 108
3.3 APPLYING THE FRAMEWORK 110
3.3.1 Cost and EfFort Estimation 110
3.3.2 Productivity Measures and Models 112
3.3.3 Data Collection 112
3.3.4 Quality Models and Measures 113
3.3.5 Reliability Models 113
3.3.6 Structural and Complexity Metrics 114
3.3.7 Management by Metrics 115
3.3.8 Evaluation of Methods and Tools 116
3.4 SOFTWARE MEASUREMENT VALIDATION 117
3.4.1 Validating Prediction Systems 117
3.4.2 Validating Measures 119
3.4.3 A Mathematical Perspective of Metrie Validation 120
3.5 PERFORMING SOFTWARE MEASUREMENT
VALIDATION 121
3.5.1 A More Stringent Requirement for Validation 122
3.5.2 Validation and Imprecise Definition 124
3.5.3 How Not to Validate 125
3.5.4 Choosing Appropriate Prediction Systems 126
viii ¦ Contents
3.6 SUMMARY 127
EXERCISES 129
FURTHER READING 131
Chapter 4 ¦ Empirical Investigation 133
4.1 PRINCIPLES OF EMPIRICAL STUDIES 134
4.1.1 Control of Variables and Study Type 135
4.1.2 Study Goals and Hypotheses 139
4.1.3 Maintaining Control over Variables 141
4.1.4 Threats to Validity 143
4.1.5 Human Subjects 144
4.2 PLANNING EXPERIMENTS 145
4.2.1 A Process Model for Performing Experiments 145
4.2.1.1 Conception 146
4.2.1.2 Design 146
4.2.1.3 Preparation 150
4.2.1.4 Execution 150
4.2.1.5 Analysis 150
4.2.1.6 Dissemination and Decision-Making 150
4.2.2 Key Experimental Design Concepts 151
4.2.2.1 Replication 153
4.2.2.2 Randomization 154
4.2.2.3 Local Control 155
4.2.3 Types of Experimental Designs 157
4.2.3.1 Crossing 159
4.2.3.2 Nesting 159
4.2.4 Selecting an Experimental Design 161
4.2.4.1 Choosing the Number of Factors 162
4.2.4.2 Factors versus Blocks 164
4.2.4.3 Choosing between Nested and Crossed
Designs 165
4.2.4.4 Fixed and Random Effects 169
Contents ¦ ix
4.2.4.5 Matched- or Same-Subject Designs 170
4.2.4.6 Repeated Measurements 170
4.3 PLANNING CASE STUDIES AS QUASI-EXPERIMENTS 171
4.3.1 Sister Projects 172
4.3.2 Baselines 172
4.3.3 Partitioned Project 173
4.3.4 Retrospective Case Study 173
4.4 RELEVANT AND MEANINGFUL STUDIES 173
4.4.1 Confirming Theories and Conventional Wisdom 174
4.4.2 Exploring Relationships 175
4.4.3 Evaluating the Accuracy of Prediction Models 177
4.4.4 Validating Measures 178
4.5 SUMMARY 179
EXERCISES 180
FURTHER READING 181
REFERENCES 182
Chapter 5 ¦ Software Metrics Data Collection 183
5.1 DEFINING GOOD DATA 184
5.2 DATA COLLECTION FOR INCIDENT REPORTS 185
5.2.1 The Problem with Problems 186
5.2.2 Failures 191
5.2.3 Faults 197
5.2.4 Changes 203
5.3 HOW TO COLLECT DATA 204
5.3.1 Data Collection Forms 208
5.3.2 Data Collection Tools 211
5.4 RELIABILITY OF DATA COLLECTION PROCEDURES 213
5.5 SUMMARY 214
EXERCISES 216
REFERENCES 222
FURTHER READING 222
x ¦ Contents
Chapter 6 ¦ Analyzing Software Measurement Data 225
6.1 STATISTICAL DISTRIBUTIONS AND
HYPOTHESIS TESTING 226
6.1.1 Probability Distributions 226
6.1.2 Hypothesis Testing Approaches 231
6.2 CLASSICAL DATA ANALYSIS TECHNIQUES 232
6.2.1 Nature of the Data 233
6.2.1.1 Sampling, Population, and Data
Distribution 233
6.2.1.2 Distribution of Software Measurements 236
6.2.1.3 Statistical Inference and Classical
Hypothesis Testing 239
6.2.2 Purpose of the Experiment 241
6.2.2.1 Confirming a Theory 241
6.2.2.2 Exploring a Relationship 242
6.2.3 Decision Tree 243
6.3 EXAMPLES OF SIMPLE ANALYSIS TECHNIQUES 243
6.3.1 BoxPlots 243
6.3.2 Bar Charts 247
6.3.3 Control Charts 248
6.3.4 Scatter Plots 250
6.3.5 Measures of Association 252
6.3.6 Robust Correlation 253
6.3.7 Linear Regression 255
6.3.8 Robust Regression 257
6.3.9 Multivariate Regression 259
6.4 MORE ADVANCED METHODS 259
6.4.1 Classification Tree Analysis 259
6.4.2 Transformations 261
6.4.3 Multivariate Data Analysis 264
6.4.3.1 Principal Component Analysis 264
6.4.3.2 Cluster Analysis 267
6.4.3.3 Discriminant Analysis 261
Contents ¦ xi
6.5 MULTICRITERIA DECISION AIDS 267
6.5.1 Basic Concepts of Multicriteria Decision-Making 268
6.5.2 Multiattribute Utility Theory 274
6.5.3 Outranking Methods 276
6.5.4 Bayesian Evaluation of Multiple Hypotheses 278
6.6 OVERVIEW OF STATISTICAL TESTS 279
6.6.1 One-Group Tests 279
6.6.1.1 BinomialTest 280
6.6.1.2 Chi-Squared Test for Goodness of Fit 280
6.6.1.3 Kolmogorov-Smirnov One-Sample Test 281
6.6.1.4 One-Sample Runs Test 281
6.6.1.5 Change-Point Test 281
6.6.2 Two-Group Tests 281
6.6.2.1 Tests to Compare Two Matched or
Related Groups 282
6.6.2.2 Tests to Compare Two Independent Groups 283
6.6.3 Comparisons Involving More than Two Groups 283
6.7 SUMMARY 284
EXERCISES 285
REFERENCE 289
FURTHER READING 289
Chapter 7 ¦ Metrics for Decision Support: The Need
for Causal Models 291
7.1 FROM CORRELATION AND REGRESSION TO
CAUSAL MODELS 293
7.2 BAYES THEOREM AND BAYESIAN NETWORKS 301
7.3 APPLYING BAYESIAN NETWORKS TO THE PROBLEM
OF SOFTWARE DEFECTS PREDICTION 306
7.3.1 A Very Simple BN for Understanding Defect
Prediction 307
7.3.2 A Füll Model for Software Defects and Reliability
Prediction 310
7.3.3 Commercial Scale Versions of the Defect
Prediction Models 314
xii ¦ Contents
7.4 BAYESIAN NETWORKS FOR SOFTWARE PROJECT
RISK ASSESSMENT AND PREDICTION 320
7.5 SUMMARY 328
EXERCISES 329
FURTHER READING 330
Part II Software Engineering Measurement
Chapter 8 ¦ Measuring Internal Product Attributes: Size 335
8.1 PROPERTIES OF SOFTWARE SIZE 336
8.2 CODE SIZE 339
8.2.1 Counting Lines of Code to Measure Code Size 339
8.2.2 Halstead s Approach 344
8.2.3 Alternative Code Size Measures 346
8.2.4 Dealing with Nontextual or External Code 347
8.3 DESIGN SIZE 348
8.4 REQUIREMENTS ANALYSIS AND SPECIFICATION
SIZE 350
8.5 FUNCTIONAL SIZE MEASURES AND ESTIMATORS 351
8.5.1 Function Points 352
8.5.1.1 Function Points for Object-Oriented
Software 355
8.5.1.2 Function Point Limitations 356
8.5.2 COCOMO II Approach 358
8.6 APPLICATIONS OF SIZE MEASURES 360
8.6.1 Using Size to Normalize Other Measurements 360
8.6.2 Size-Based Reuse Measurement 361
8.6.3 Size-Based Software Testing Measurement 363
8.7 PROBLEM, SOLUTION SIZE, COMPUTATIONAL
COMPLEXITY 364
8.8 SUMMARY 365
EXERCISES 366
FURTHER READING 368
Contents ¦ xiii
Chapter 9 ¦ Measuring Internal Product Attributes:
Structure 374
9.1 ASPECTS OF STRUCTURAL MEASURES 372
9.1.1 Structural Complexity Properties 373
9.1.2 Length Properties 373
9.1.3 Coupling Properties 374
9.1.4 Cohesion Properties 375
9.1.5 Properties of Custom Attributes 375
9.2 CONTROL FLOW STRUCTURE OF PROGRAM UNITS 376
9.2.1 Flowgraph Model and the Notion of Structured
Programs 377
9.2.1.1 Sequencing and Nesting 381
9.2.1.2 Generalized Notion of Structuredness 384
9.2.1.3 Prime Decomposition 386
9.2.2 Hierarchical Measures 388
9.2.2.1 McCabe s Cyclomatic Complexity Measure 391
9.2.2.2 McCabe s Essential Complexity Measure 393
9.2.3 Code Structure and Test Coverage Measures 394
9.2.3.1 Minimum Number of Test Cases 399
9.2.3.2 Test Effectiveness Ratio 401
9.3 DESIGN-LEVEL ATTRIBUTES 402
9.3.1 Models of Modularity and Information Flow 402
9.3.2 Global Modularity 404
9.3.3 Morphology 405
9.3.4 Tree Impurity 406
9.3.5 Internal Reuse 409
9.3.6 Information Flow 410
9.3.7 Information Flow: Test Coverage Measures 412
9.4 OBJECT-ORIENTED STRUCTURAL ATTRIBUTES
AND MEASURES 414
9.4.1 Measuring Coupling in Object-Oriented Systems 416
9.4.2 Measuring Cohesion in Object-Oriented Systems 418
9.4.3 Object-Oriented Length Measures 421
xiv ¦ Contents
9.4.4 Object-Oriented Reuse Measurement 422
9.4.5 Design Pattern Use 423
9.5 NO SINGLE OVERALL SOFTWARE COMPLEXITY
ME ASURE 425
9.6 SUMMARY 428
EXERCISES 429
APPENDICES TO CHAPTER 9 433
A. 1 McCabe s Testing Strategy 433
A.l.l Background 433
A.1.2 The Strategy 434
A.2 Computing Test Coverage Measures 436
FURTHER READING 437
Chapter 10 ¦ Measuring External Product Attributes 441
10.1 MODELING SOFTWARE QUALITY 442
10.1.1 Early Models 443
10.1.2 Define-Your-Own Models 447
10.1.3 ISO/IEC 9126-1 and ISO/IEC 25010 Standard
Quality Models 447
10.2 MEASURING ASPECTS OF QUALITY 449
10.2.1 Defects-Based Quality Measures 450
10.2.1.1 Defect Density Measures 450
10.2.1.2 Other Quality Measures Based on Defect
Counts 455
10.3 USABILITY MEASURES 456
10.3.1 External View ofUsability 457
10.3.2 Internal Attributes Affecting Usability 459
10.4 MAINTAINABILITY MEASURES 460
10.4.1 External View of Maintainability 462
10.4.2 Internal Attributes Affecting Maintainability 463
10.5 SECURITY MEASURES 466
10.5.1 External View of Security 467
10.5.2 Internal Attributes Affecting Security 470
Contents ¦ xv
10.6 SUMMARY 470
EXERCISES 471
FURTHER READING 473
Chapter 11 ¦ Software Reliability: Measurement
and Prediction 475
11.1 BASICS OF RELIABILITY THEORY 476
11.2 THE SOFTWARE RELIABILITY PROBLEM 484
11.3 PARAMETRIC RELIABILITY GROWTH MODELS 490
11.3.1 The Jelinski-Moranda Model 492
11.3.2 Other Models Based on JM 494
11.3.3 The Littlewood Model 495
11.3.4 The Littlewood-Verrall Model 495
11.3.5 Nonhomogeneous Poisson Process Models 496
11.3.6 General Comments on the Models 497
11.4 PREDICTIVE ACCURACY 497
11.4.1 Dealing with Bias: The u-Plot 499
11.4.2 Dealing with Noise 502
11.4.3 Prequential Likelihood Function 503
11.4.4 Choosing the Best Model 508
11.5 RECALIBRATION OF SOFTWARE RELIABILITY
GROWTH PREDICTIONS 508
11.6 IMPORTANCE OF THE OPERATIONAL
ENVIRONMENT 517
11.7 WIDER ASPECTS OF SOFTWARE RELIABILITY 518
11.8 SUMMARY 523
EXERCISES 523
FURTHER READING 526
APPENDIX: SOLUTIONS TO SELECTED EXERCISES, 529
BIBLIOGRAPHY, 555
INDEX, 577
|
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| spelling | Fenton, Norman E. 1956- Verfasser (DE-588)172073669 aut Software metrics a rigorous and practical approach Norman E. Fenton ; James Bieman 3. ed. Boca Raton, Fla. [u.a.] CRC Press 2015 XXI, 595 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Previous ed.: Boston, Mass.; London: PWS, 1996 Includes bibliographical references and index Software measurement Software measurement / Case studies Software measurement / Problems, exercises, etc Computer software / Quality control Softwaremetrie (DE-588)4227925-2 gnd rswk-swf (DE-588)4522595-3 Fallstudiensammlung gnd-content Softwaremetrie (DE-588)4227925-2 s DE-604 Bieman, James Sonstige oth HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026885911&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Fenton, Norman E. 1956- Software metrics a rigorous and practical approach Software measurement Software measurement / Case studies Software measurement / Problems, exercises, etc Computer software / Quality control Softwaremetrie (DE-588)4227925-2 gnd |
| subject_GND | (DE-588)4227925-2 (DE-588)4522595-3 |
| title | Software metrics a rigorous and practical approach |
| title_auth | Software metrics a rigorous and practical approach |
| title_exact_search | Software metrics a rigorous and practical approach |
| title_full | Software metrics a rigorous and practical approach Norman E. Fenton ; James Bieman |
| title_fullStr | Software metrics a rigorous and practical approach Norman E. Fenton ; James Bieman |
| title_full_unstemmed | Software metrics a rigorous and practical approach Norman E. Fenton ; James Bieman |
| title_short | Software metrics |
| title_sort | software metrics a rigorous and practical approach |
| title_sub | a rigorous and practical approach |
| topic | Software measurement Software measurement / Case studies Software measurement / Problems, exercises, etc Computer software / Quality control Softwaremetrie (DE-588)4227925-2 gnd |
| topic_facet | Software measurement Software measurement / Case studies Software measurement / Problems, exercises, etc Computer software / Quality control Softwaremetrie Fallstudiensammlung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026885911&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT fentonnormane softwaremetricsarigorousandpracticalapproach AT biemanjames softwaremetricsarigorousandpracticalapproach |