MARC details
| 000 -LEADER |
|---|
| fixed length control field |
13070cam a2200337 i 4500 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9781118959312 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
1118959310 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9781118959329 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
1118959329 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9781118959305 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
1118959302 |
| 040 ## - CATALOGING SOURCE |
|---|
| Transcribing agency |
CUS |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Abran, Alain, |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Software project estimation : |
| Sub title |
the fundamentals for providng high quality information to decision makers / |
| Statement of responsibility, etc. |
Alain Abran. |
| 260 #1 - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc. |
Hoboken, New Jersey : |
| Name of publisher, distributor, etc. |
John Wiley & Sons Inc., |
| Date of publication, distribution, etc. |
[2015] |
| 300 ## - DESCRIPTION |
|---|
| Extent |
1 online resource. |
| 505 0# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
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. |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
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. |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
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. |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
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. |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
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. |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
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. |
| 650 #0 - SUBJECT |
|---|
| Keyword |
Computer software |
| 650 #7 - SUBJECT |
|---|
| Keyword |
Computer software |
| 856 40 - ONLINE RESOURCES |
|---|
| url |
https://doi.org/10.1002/9781118959312 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Koha item type |
e-Books |
