Foreword xiii
Overview xvii
Acknowledgments xxiii
About the Author xxv
Part One Understanding the Estimation Process 1
1. The Estimation Process: Phases and Roles 3
1.1. Introduction 3
1.2. Generic Approaches in Estimation Models: Judgment or Engineering? 4
1.3. Overview of Software Project Estimation and Current Practices 6
1.4. Levels of Uncertainty in an Estimation Process 11
1.5. Productivity Models 14
1.6. The Estimation Process 16
1.7. Budgeting and Estimating: Roles and Responsibilities 23
1.8. Pricing Strategies 27
1.9. Summary â" Estimating Process, Roles, and Responsibilities 28
2. Engineering and Economics Concepts for Understanding Software Process Performance 32
2.1. Introduction: The Production (Development) Process 32
2.2. The Engineering (and Management) Perspective on a Production Process 34
2.3. Simple Quantitative Process Models 36
2.4. Quantitative Models and Economics Concepts 45
2.5. Software Engineering Datasets and Their Distribution 49
2.6. Productivity Models: Explicit and Implicit Variables 52
2.7. A Single and Universal Catch-All Multidimensional Model or Multiple Simpler Models? 54
3. Project Scenarios, Budgeting, and Contingency Planning 60
3.1. Introduction 60
3.2. Project Scenarios for Estimation Purposes 61
3.3. Probability of Underestimation and Contingency Funds 65
3.4. A Contingency Example for a Single Project 67
3.5. Managing Contingency Funds at the Portfolio Level 69
3.6. Managerial Prerogatives: An Example in the AGILE Context 69
3.7. Summary 71
Part Two Estimation Process: What Must be Verified? 77
4. What Must be Verified in an Estimation Process: An Overview 79
4.1. Introduction 79
4.2. Verification of the Direct Inputs to An Estimation Process 81
4.3. Verification of the Productivity Model 84
4.4. Verification of the Adjustment Phase 86
4.5. Verification of the Budgeting Phase 87
4.6. Re-Estimation and Continuous Improvement to the Full Estimation Process 88
5. Verification of the Dataset Used to Build the Models 94
5.1. Introduction 94
5.2. Verification of DIRECT Inputs 96
5.3. Graphical Analysis â" One-Dimensional 100
5.4. Analysis of the Distribution of the Input Variables 102
5.5. Graphical Analysis â" Two-Dimensional 108
5.6. Size Inputs Derived from a Conversion Formula 111
5.7. Summary 112
6. Verification of Productivity Models 119
6.1. Introduction 119
6.2. Criteria Describing the Relationships Across Variables 120
6.3. Verification of the Assumptions of the Models 125
6.4. Evaluation of Models by Their Own Builders 127
6.5. Models Already Builtâ"Should You Trust Them? 128
6.6. Lessons Learned: Distinct Models by Size Range 133
6.7. Summary 138
7. Verification of the Adjustment Phase 141
7.1. Introduction 141
7.2. Adjustment Phase in the Estimation Process 142
7.3. The Bundled Approach in Current Practices 145
7.4. Cost Drivers as Estimation Submodels! 148
7.5. Uncertainty and Error Propagation 151
Part Three Building Estimation Models: Data Collection and Analysis 159
8. Data Collection and Industry Standards: The ISBSG Repository 161
8.1. Introduction: Data Collection Requirements 161
8.2. The International Software Benchmarking Standards Group 163
8.3. ISBSG Data Collection Procedures 165
8.4. Completed ISBSG Individual Project Benchmarking Reports: Some Examples 170
8.5. Preparing to Use the ISBSG Repository 173
9. Building and Evaluating Single Variable Models 185
9.1. Introduction 185
9.2. Modestly, One Variable at a Time 186
9.3. Data Preparation 189
9.4. Analysis of the Quality and Constraints of Models 193
9.5. Other Models by Programming Language 196
9.6. Summary 202
10. Building Models with Categorical Variables 205
10.1. Introduction 205
10.2. The Available Dataset 206
10.3. Initial Model with a Single Independent Variable 208
10.4. Regression Models with Two Independent Variables 210
11. Contribution of Productivity Extremes in Estimation 218
11.1. Introduction 218
11.2. Identification of Productivity Extremes 219
11.3. Investigation of Productivity Extremes 220
11.4. Lessons Learned for Estimation Purposes 224
12. Multiple Models from a Single Dataset 227
12.1. Introduction 227
12.2. Low and High Sensitivity to Functional Size Increases: Multiple Models 228
12.3. The Empirical Study 230
12.4. Descriptive Analysis 231
12.5. Productivity Analysis 234
12.6. External Benchmarking with the ISBSG Repository 238
12.7. Identification of the Adjustment Factors for Model Selection 241
13. Re-Estimation: A Recovery Effort Model 244
13.1. Introduction 244
13.2. The Need for Re-Estimation and Related Issues 245
13.3. The Recovery Effort Model 246
13.4. A Recovery Model When a Re-Estimation Need is Recognized at Time T > 0 248
Exercises 251
Term Assignments 251
References 253
Index 257
