Software engineering metrics and models

목차 (Table of Contents)

• CONTENTS
• 1 The Role of Metrics and Models in Software Development = 1
• 1.1 Introduction = 1
• 1.2 The Software Development Process = 3
• 1.2.1 The Software Life Cycle = 4
• 1.2.2 The Quality of Software = 7
• 1.2.3 Characteristics of Large-scale Systems = 9
• 1.2.4 The Need for Software Engineering = 14
• 1.3 Software Metrics and Models = 16
• 1.3.1 Software Complexity Metrics = 17
• 1.3.2 Objective and Algorithmic Measurements = 17
• 1.3.3 Process and Product Metrics = 19
• 1.3.4 Models of the Software Development Process = 20
• 1.3.5 Meta-metrics = 21
• 1.4 Empirical Validation of Development Models = 22
• 1.4.1 Data Collection = 23
• 1.4.2 The Statistical Interpretation of Data = 26
• 1.5 The Limitations of Metrics and Models = 27
• 1.6 Organization of This Book = 28
• Exercises = 29
• 2 Software Metrics = 30
• 2.1 Introduction = 30
• 2.2 Size Metrics = 32
• 2.2.1 Lines of Code = 34
• 2.2.2 Token Count = 36
• 2.2.3 Function Count = 42
• 2.2.4 Equivalent Size Measures = 44
• 2.3 Data Structure Metrics = 47
• 2.3.1 The Amount of Data = 48
• 2.3.2 The Usage of Data within a Module = 52
• 2.3.3 The Sharing of Data among Modules = 57
• 2.4 Logic Structure Metrics = 59
• 2.4.1 Decision Count = 62
• 2.4.2 Minimum Number of Paths and Reachability Metrics = 70
• 2.4.3 Nesting Levels = 74
• 2.4.4 Transfer Usage = 76
• 2.5 Composite Metrics = 78
• 2.6 Software Science Composite Metrics = 80
• 2.6.1 The Estimated Program Length = 81
• 2.6.2 The Program Volume = 82
• 2.6.3 Potential Volume and Difficulty = 82
• 2.6.4 Effort and Time = 84
• 2.6.5 Language Level = 85
• 2.6.6 The Real Contributions of Software Science = 87
• 2.7 Effort and Cost Metrics = 87
• 2.7.1 Micro-levels of Effort and Cost = 89
• 2.7.2 Macro-levels of Effort and Cost = 91
• 2.8 Defects and Reliability = 93
• 2.8.1 Defect Metrics and the Software Life Cycle = 94
• 2.8.2 Discovering and Correcting Defects = 99
• 2.8.3 Software Reliability = 102
• 2.8.4 The Cost of Repairing Defects = 105
• 2.9 Design Metrics = 106
• 2.10 Summary and Conclusions = 109
• Exercises = 110
• 3 Measurement and Analysis = 113
• 3.1 Introduction = 113
• 3.2 Historical Records = 114
• 3.2.1 The Difficulty of Gathering Data = 114
• 3.2.2 Data Transportability = 115
• 3.2.3 The Aging of Data = 116
• 3.3 Controlled Experiments = 117
• 3.3.1 The Validity of Experiments = 117
• 3.3.2 Pre-experimental Designs = 119
• 3.3.3 The Pretest-Posttest Design = 122
• 3.3.4 The Posttest-Only Design = 124
• 3.3.5 Counter-Balanced Design = 124
• 3.4 Statistical Analysis = 127
• 3.4.1 Types of Measurement Scales = 127
• 3.4.2 Measures of Central Tendency and Variability = 130
• 3.4.3 The Experimental Paradigm = 134
• 3.4.4 Relationships among Sets of Measures = 141
• 3.5 Model Evaluation Criteria = 165
• 3.5.1 The Coefficient of Multiple Determination (R²) = 168
• 3.5.2 The Relative Error (RE) and the Mean Relative Error (RE) = 172
• 3.5.3 The Magnitude and the Mean Magnitude of Relative Error (MRE and MRE) = 172
• 3.5.4 Prediction at Level 1 (PRED(1)) = 173
• 3.5.5 The Mean Squared Error (SE) and the Relative Root Mean Squared Error (RMS) = 173
• 3.5.6 The Mean Squared Error(SE) and the Relative Root Mean Squared Error(RMS) = 173
• 3.6 Data Collection Used in this Book = 177
• 3.6.1 Size in Lines of Code (Ss or S) = 177
• 3.6.2 Effort in person-hours or Person-months (E) = 177
• 3.6.3 Development duration (T) = 177
• 3.6.4 Token Counts = 178
• 3.6.5 Defect Counts = 178
• 3.6.6 Description of Several Collections of Commercial Data = 178
• 3.7 Summary and Conclusions = 180
• Exercises = 181
• 4 Small Scale Experiments, Micro-models of Effort, and Programming Techniques = 183
• 4.1 Introduction = 183
• 4.2 Small-Scale Experiments = 186
• 4.2.1 The Construction Process = 186
• 4.2.2 The maintenance and Comprehension Processes = 187
• 4.3 Micro-models of Effort = 189
• 4.3.1 Halstead's Time Estimator Study = 189
• 4.3.2 Woodfield's Time Estimator Study = 193
• 4.3.3 Modularity = 197
• 4.3.4 Basili's Program Construction Model = 206
• 4.3.5 Gordeon's Comprehension Study = 209
• 4.3.6 Curtis' Debugging Study = 211
• 4.4 Early Size Estimation = 213
• 4.5 Experiments on Programming Techniques = 218
• 4.5.1 Comments = 219
• 4.5.2 Mnemonic Terms = 221
• 4.5.3 Transfer of Control = 223
• 4.5.4 Flowcharts = 226
• 4.5.5 Debugging Aids = 229
• 4.5.6 Summary and Conclusions = 231
• Exercises = 232
• 5 Macro-Models o Productivity = 234
• 5.1 Introduction = 234
• 5.2 Factors Affecting Productivity = 237
• 5.3 Macro-level Studies on Certain Factors Affecting Productivity = 240
• 5.4 The Walston-Felix Study of Productivity = 243
• 5.5 Structured Programming and Its Effect on Productivity = 249
• 5.6 The ITT Study of Productivity = 251
• 5.7 Productivity Ranges in the COCOMO Model = 254
• 5.8 The Effect of Team Size on Productivity = 258
• 5.9 The Effect of Project Size on Productivity = 268
• 5.10 Summary and Conclusions = 272
• Exercises = 272
• 6 Macro-models for Effort Estimation = 274
• 6.1 Introduction = 274
• 6.2 Historical - Experiential Models = 277
• 6.3 Statistically Based Models = 279
• 6.3.1 Linear Statistical Models = 279
• 6.3.2 Nonlinear Statistical Models = 281
• 6.3.3 Results of the Walston-Felix Study = 283
• 6.3.4 Results of the Bailey-Basili Study = 284
• 6.4 Theoretically Based Models = 287
• 6.4.1 Putnam's Resource Allocation Model = 288
• 6.4.2 Jensen's Model = 295
• 6.4.3 The Software Science Effort Macro Model = 296
• 6.5 A Composite Model - COCOMO = 300
• 6.5.1 Validation of the COCOMO Model = 303
• 6.5.2 Evaluation of the COCOMO Model = 304
• 6.5.3 Results of Basic COCOMO Applied to Other Databases = 305
• 6.5.4 A Modification of COCOMO Parameters = 307
• 6.6 A Composite Model - SOFTCOST = 307
• 6.7 Effect of Team Size on Effort - The COPMO Model = 310
• 6.7.1 The Interchangeability of Personnel and Time = 310
• 6.7.2 The Generalized COPMO Model = 318
• 6.7.3 The Use of COPMO for Effort Predication = 322
• 6.7.4 Effort Complexity Classes and the Calibration Procedure = 326
• 6.8 Summary and Conclusions = 329
• Exercises = 330
• 7 Defect Models = 332
• 7.1 Introduction = 332
• 7.2 Static Models of Defects = 333
• 7.2.1 Akiyama's Study = 334
• 7.2.2 Motley and Brooks' Study = 336
• 7.2.3 Studies by Halstead and Ottenstein = 338
• 7.2.4 Potier's Study = 340
• 7.2.5 Shen's Study = 343
• 7.2.6 Removing the Effect of Module Size = 347
• 7.3 Dynamic Models of Defects = 350
• 7.3.1 The Musa Model = 353
• 7.3.2 Other Dynamic Models = 355
• 7.4 Summary and Conclusions = 357
• Exercises = 357
• 8 The Future of Software Engineering Metrics and Models = 359
• 8.1 Introduction = 359
• 8.2 Quantitative Assessment of Software Engineering Techniques = 361
• 8.3 A New Software Development Paradigm = 363
• 8.4 A Last Word - Looking Forward = 364
• References = 366
• Appendix A = 376
• Appendix B = 386
• Illustration Credits = 391
• Index = 392