Software project estimation : the fundamentals for providng high quality information to decision makers /

• Κύριος συγγραφέας: Abran, Alain, 1949-
• Μορφή: Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Hoboken, New Jersey : John Wiley & Sons Inc., [2015]
• Θέματα: Computer software > Development > Estimates.; Electronic books.

Software projects are often late and over-budget and this leads to major problems for software customers. Clearly, there is a serious issue in estimating a realistic, software project budget. Furthermore, generic estimation models cannot be trusted to provide credible estimates for projects as compl...

Πίνακας περιεχομένων:

• 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
• 1.2.1. Practitioner's Approach: Judgment and Craftsmanship 4
• 1.2.2. Engineering Approach: Modest-One Variable at a Time 5
• 1.3. Overview of Software Project Estimation and Current Practices 6
• 1.3.1. Overview of an Estimation Process 6
• 1.3.2. Poor Estimation Practices 7
• 1.3.3. Examples of Poor Estimation Practices 9
• 1.3.4. The Reality: A Tally of Failures 10
• 1.4. Levels of Uncertainty in an Estimation Process 11
• 1.4.1. The Cone of Uncertainty 11
• 1.4.2. Uncertainty in a Productivity Model 12
• 1.5. Productivity Models 14
• 1.6. The Estimation Process 16
• 1.6.1. The Context of the Estimation Process 16
• 1.6.2. The Foundation: The Productivity Model 17
• 1.6.3. The Full Estimation Process 18
• 1.7. Budgeting and Estimating: Roles and Responsibilities 23
• 1.7.1. Project Budgeting: Levels of Responsibility 23
• 1.7.2. The Estimator 25
• 1.7.3. The Manager (Decision-Taker and Overseer) 25
• 1.8. Pricing Strategies 27
• 1.8.1. Customers-Suppliers: The Risk Transfer Game in Estimation 28
• 1.9. Summary - Estimating Process, Roles, and Responsibilities 28
• Exercises 30
• Term Assignments 31
• 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
• 2.3.1. Productivity Ratio 36
• 2.3.2. Unit Effort (or Unit Cost) Ratio 38
• 2.3.3. Averages 39
• 2.3.4. Linear and Non-Linear Models 42
• 2.4. Quantitative Models and Economics Concepts 45
• 2.4.1. Fixed and Variable Costs 45
• 2.4.2. Economies and Diseconomies of Scale 48
• 2.5. Software Engineering Datasets and Their Distribution 49.
• 2.5.1. Wedge-Shaped Datasets 49
• 2.5.2. Homogeneous Datasets 50
• 2.6. Productivity Models: Explicit and Implicit Variables 52
• 2.7. A Single and Universal Catch-All Multidimensional Model or Multiple Simpler Models? 54
• 2.7.1. Models Built from Available Data 55
• 2.7.2. Models Built on Opinions on Cost Drivers 55
• 2.7.3. Multiple Models with Coexisting Economies and Diseconomies of Scale 56
• Exercises 58
• Term Assignments 59
• 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
• Further Reading: A Simulation for Budgeting at the Portfolio Level 71
• Exercises 74
• Term Assignments 75
• 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
• 4.2.1. Identification of the Estimation Inputs 81
• 4.2.2. Documenting the Quality of These Inputs 82
• 4.3. Verification of the Productivity Model 84
• 4.3.1. In-House Productivity Models 84
• 4.3.2. Externally Provided Models 85
• 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
• Further Reading: The Estimation Verification Report 89
• Exercises 92
• Term Assignments 93
• 5. Verification of the Dataset Used to Build the Models 94
• 5.1. Introduction 94
• 5.2. Verification of DIRECT Inputs 96
• 5.2.1. Verification of the Data Definitions and Data Quality 96
• 5.2.2. Importance of the Verification of the Measurement Scale Type 97
• 5.3. Graphical Analysis - One-Dimensional 100.
• 5.4. Analysis of the Distribution of the Input Variables 102
• 5.4.1. Identification of a Normal (Gaussian) Distribution 102
• 5.4.2. Identification of Outliers: One-Dimensional Representation 103
• 5.4.3. Log Transformation 107
• 5.5. Graphical Analysis - Two-Dimensional 108
• 5.6. Size Inputs Derived from a Conversion Formula 111
• 5.7. Summary 112
• Further Reading: Measurement and Quantification 113
• Exercises 116
• Term Assignments 117
• Exercises-Further Reading Section 117
• Term Assignments-Further Reading Section 118
• 6. Verification of Productivity Models 119
• 6.1. Introduction 119
• 6.2. Criteria Describing the Relationships Across Variables 120
• 6.2.1. Simple Criteria 120
• 6.2.2. Practical Interpretation of Criteria Values 122
• 6.2.3. More Advanced Criteria 124
• 6.3. Verification of the Assumptions of the Models 125
• 6.3.1. Three Key Conditions Often Required 125
• 6.3.2. Sample Size 126
• 6.4. Evaluation of Models by Their Own Builders 127
• 6.5. Models Already Built-Should You Trust Them? 128
• 6.5.1. Independent Evaluations: Small-Scale Replication Studies 128
• 6.5.2. Large-Scale Replication Studies 129
• 6.6. Lessons Learned: Distinct Models by Size Range 133
• 6.6.1. In Practice, Which is the Better Model? 138
• 6.7. Summary 138
• Exercises 139
• Term Assignments 139
• 7. Verification of the Adjustment Phase 141
• 7.1. Introduction 141
• 7.2. Adjustment Phase in the Estimation Process 142
• 7.2.1. Adjusting the Estimation Ranges 142
• 7.2.2. The Adjustment Phase in the Decision-Making Process: Identifying Scenarios for Managers 144
• 7.3. The Bundled Approach in Current Practices 145
• 7.3.1. Overall Approach 145
• 7.3.2. Detailed Approach for Combining the Impact of Multiple Cost Drivers in Current Models 146
• 7.3.3. Selecting and Categorizing Each Adjustment: The Transformation of Nominal Scale Cost Drivers into /Numbers 147
• 7.4. Cost Drivers as Estimation Submodels! 148
• 7.4.1. Cost Drivers as Step Functions 148.
• 7.4.2. Step Function Estimation Submodels with Unknown Error Ranges 149
• 7.5. Uncertainty and Error Propagation 151
• 7.5.1. Error Propagation in Mathematical Formulas 151
• 7.5.2. The Relevance of Error Propagation in Models 153
• Exercises 156
• Term Assignments 157
• 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
• 8.2.1. The ISBSG Organization 163
• 8.2.2. The ISBSG Repository 164
• 8.3. ISBSG Data Collection Procedures 165
• 8.3.1. The Data Collection Questionnaire 165
• 8.3.2. ISBSG Data Definitions 167
• 8.4. Completed ISBSG Individual Project Benchmarking Reports: Some Examples 170
• 8.5. Preparing to Use the ISBSG Repository 173
• 8.5.1. ISBSG Data Extract 173
• 8.5.2. Data Preparation: Quality of the Data Collected 173
• 8.5.3. Missing Data: An Example with Effort Data 175
• Further Reading 1: Benchmarking Types 177
• Further Reading 2: Detailed Structure of the ISBSG Data Extract 179
• Exercises 183
• Term Assignments 183
• 9. Building and Evaluating Single Variable Models 185
• 9.1. Introduction 185
• 9.2. Modestly, One Variable at a Time 186
• 9.2.1. The Key Independent Variable: Software Size 186
• 9.2.2. Analysis of the Work-Effort Relationship in a Sample 188
• 9.3. Data Preparation 189
• 9.3.1. Descriptive Analysis 189
• 9.3.2. Identifying Relevant Samples and Outliers 189
• 9.4. Analysis of the Quality and Constraints of Models 193
• 9.4.1. Small Projects 195
• 9.4.2. Larger Projects 195
• 9.4.3. Implication for Practitioners 195
• 9.5. Other Models by Programming Language 196
• 9.6. Summary 202
• Exercises 203
• Term Assignments 203
• 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.
• 10.3.1. Simple Linear Regression Model with Functional Size Only 208
• 10.3.2. Nonlinear Regression Models with Functional Size 208
• 10.4. Regression Models with Two Independent Variables 210
• 10.4.1. Multiple Regression Models with Two Independent Quantitative Variables 210
• 10.4.2. Multiple Regression Models with a Categorical Variable: Project Difficulty 210
• 10.4.3. The Interaction of Independent Variables 215
• Exercises 216
• Term Assignments 217
• 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
• 11.3.1. Projects with Very Low Unit Effort 221
• 11.3.2. Projects with Very High Unit Effort 222
• 11.4. Lessons Learned for Estimation Purposes 224
• Exercises 225
• Term Assignments 225
• 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
• 12.3.1. Context 230
• 12.3.2. Data Collection Procedures 231
• 12.3.3. Data Quality Controls 231
• 12.4. Descriptive Analysis 231
• 12.4.1. Project Characteristics 231
• 12.4.2. Documentation Quality and Its Impact on Functional Size Quality 233
• 12.4.3. Unit Effort (in Hours) 234
• 12.5. Productivity Analysis 234
• 12.5.1. Single Model with the Full Dataset 234
• 12.5.2. Model of the Least Productive Projects 235
• 12.5.3. Model of the Most Productive Projects 237
• 12.6. External Benchmarking with the ISBSG Repository 238
• 12.6.1. Project Selection Criteria and Samples 238
• 12.6.2. External Benchmarking Analysis 239
• 12.6.3. Further Considerations 240
• 12.7. Identification of the Adjustment Factors for Model Selection 241
• 12.7.1. Projects with the Highest Productivity (i.e., the Lowest Unit Effort) 241
• 12.7.2. Lessons Learned 242
• Exercises 243
• Term Assignments 243
• 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
• 13.3.1. Key Concepts 246
• 13.3.2. Ramp-Up Process Losses 247
• 13.4. A Recovery Model When a Re-Estimation Need is Recognized at Time T > 0 248
• 13.4.1. Summary of Recovery Variables 248
• 13.4.2. A Mathematical Model of a Recovery Course in Re-Estimation 248
• 13.4.3. Probability of Underestimation −p(u) 249
• 13.4.4. Probability of Acknowledging the Underestimation on a Given Month −p(t) 250
• Exercises 251
• Term Assignments 251
• References 253
• Index 257.