Software Project Estimation

Alain Abran, PhD, is a Professor and the Director of the Software Engineering Research Laboratory at Université du Québec, Canada. He is also Co-Chair of the Common Software Measurement International Consortium. He was the international secretary for ISO/IEC JTC1 SC7. Dr. Abran has over 20 years of industry experience in information systems development and software engineering.

• Foreword xiiiOverview xviiAcknowledgments xxiiiAbout the Author xxvPart One Understanding the Estimation Process 11. The Estimation Process: Phases and Roles 31.1. Introduction 31.2. Generic Approaches in Estimation Models: Judgment or Engineering? 41.3. Overview of Software Project Estimation and Current Practices 61.4. Levels of Uncertainty in an Estimation Process 111.5. Productivity Models 141.6. The Estimation Process 161.7. Budgeting and Estimating: Roles and Responsibilities 231.8. Pricing Strategies 271.9. Summary – Estimating Process, Roles, and Responsibilities 282. Engineering and Economics Concepts for Understanding Software Process Performance 322.1. Introduction: The Production (Development) Process 322.2. The Engineering (and Management) Perspective on a Production Process 342.3. Simple Quantitative Process Models 362.4. Quantitative Models and Economics Concepts 452.5. Software Engineering Datasets and Their Distribution 492.6. Productivity Models: Explicit and Implicit Variables 522.7. A Single and Universal Catch-All Multidimensional Model or Multiple Simpler Models? 543. Project Scenarios, Budgeting, and Contingency Planning 603.1. Introduction 603.2. Project Scenarios for Estimation Purposes 613.3. Probability of Underestimation and Contingency Funds 653.4. A Contingency Example for a Single Project 673.5. Managing Contingency Funds at the Portfolio Level 693.6. Managerial Prerogatives: An Example in the AGILE Context 693.7. Summary 71Part Two Estimation Process: What Must be Verified? 774. What Must be Verified in an Estimation Process: An Overview 794.1. Introduction 794.2. Verification of the Direct Inputs to An Estimation Process 814.3. Verification of the Productivity Model 844.4. Verification of the Adjustment Phase 864.5. Verification of the Budgeting Phase 874.6. Re-Estimation and Continuous Improvement to the Full Estimation Process 885. Verification of the Dataset Used to Build the Models 945.1. Introduction 945.2. Verification of DIRECT Inputs 965.3. Graphical Analysis – One-Dimensional 1005.4. Analysis of the Distribution of the Input Variables 1025.5. Graphical Analysis – Two-Dimensional 1085.6. Size Inputs Derived from a Conversion Formula 1115.7. Summary 1126. Verification of Productivity Models 1196.1. Introduction 1196.2. Criteria Describing the Relationships Across Variables 1206.3. Verification of the Assumptions of the Models 1256.4. Evaluation of Models by Their Own Builders 1276.5. Models Already Built–Should You Trust Them? 1286.6. Lessons Learned: Distinct Models by Size Range 1336.7. Summary 1387. Verification of the Adjustment Phase 1417.1. Introduction 1417.2. Adjustment Phase in the Estimation Process 1427.3. The Bundled Approach in Current Practices 1457.4. Cost Drivers as Estimation Submodels! 1487.5. Uncertainty and Error Propagation 151Part Three Building Estimation Models: Data Collection and Analysis 1598. Data Collection and Industry Standards: The ISBSG Repository 1618.1. Introduction: Data Collection Requirements 1618.2. The International Software Benchmarking Standards Group 1638.3. ISBSG Data Collection Procedures 1658.4. Completed ISBSG Individual Project Benchmarking Reports: Some Examples 1708.5. Preparing to Use the ISBSG Repository 1739. Building and Evaluating Single Variable Models 1859.1. Introduction 1859.2. Modestly, One Variable at a Time 1869.3. Data Preparation 1899.4. Analysis of the Quality and Constraints of Models 1939.5. Other Models by Programming Language 1969.6. Summary 20210. Building Models with Categorical Variables 20510.1. Introduction 20510.2. The Available Dataset 20610.3. Initial Model with a Single Independent Variable 20810.4. Regression Models with Two Independent Variables 21011. Contribution of Productivity Extremes in Estimation 21811.1. Introduction 21811.2. Identification of Productivity Extremes 21911.3. Investigation of Productivity Extremes 22011.4. Lessons Learned for Estimation Purposes 22412. Multiple Models from a Single Dataset 22712.1. Introduction 22712.2. Low and High Sensitivity to Functional Size Increases: Multiple Models 22812.3. The Empirical Study 23012.4. Descriptive Analysis 23112.5. Productivity Analysis 23412.6. External Benchmarking with the ISBSG Repository 23812.7. Identification of the Adjustment Factors for Model Selection 24113. Re-Estimation: A Recovery Effort Model 24413.1. Introduction 24413.2. The Need for Re-Estimation and Related Issues 24513.3. The Recovery Effort Model 24613.4. A Recovery Model When a Re-Estimation Need is Recognized at Time T > 0 248Exercises 251Term Assignments 251References 253Index 257