Software Project Estimation: The Fundamentals for Providing High Quality Information to Decision Makers

Cover
Title Page
Copyright
Contents
Foreword
Overview
Acknowledgments
About the Author
Part I Understanding the Estimation Process
	Chapter 1 The Estimation Process: Phases and Roles
		1.1 Introduction
		1.2 Generic Approaches in Estimation Models: Judgment or Engineering?
			1.2.1 Practitioner\'s Approach: Judgment and Craftsmanship
			1.2.2 Engineering Approach: Modest-One Variable at a Time
		1.3 Overview of Software Project Estimation and Current Practices
			1.3.1 Overview of an Estimation Process
			1.3.2 Poor Estimation Practices
			1.3.3 Examples of Poor Estimation Practices
			1.3.4 The Reality: A Tally of Failures
		1.4 Levels of Uncertainty in an Estimation Process
			1.4.1 The Cone of Uncertainty
			1.4.2 Uncertainty in a Productivity Model
		1.5 Productivity Models
		1.6 The Estimation Process
			1.6.1 The Context of the Estimation Process
			1.6.2 The Foundation: The Productivity Model
			1.6.3 The Full Estimation Process
		1.7 Budgeting and Estimating: Roles and Responsibilities
			1.7.1 Project Budgeting: Levels of Responsibility
			1.7.2 The Estimator
			1.7.3 The Manager (Decision-Taker and Overseer)
		1.8 Pricing Strategies
			1.8.1 Customers-Suppliers: The Risk Transfer Game in Estimation
		1.9 Summary - Estimating Process, Roles, and Responsibilities
		Exercises
		Term Assignments
	Chapter 2 Engineering and Economics Concepts for Understanding Software Process Performance
		2.1 Introduction: The Production (Development) Process
		2.2 The Engineering (and Management) Perspective on a Production Process
		2.3 Simple Quantitative Process Models
			2.3.1 Productivity Ratio
			2.3.2 Unit Effort (or Unit Cost) Ratio
			2.3.3 Averages
			2.3.4 Linear and Non-Linear Models
		2.4 Quantitative Models and Economics Concepts
			2.4.1 Fixed and Variable Costs
			2.4.2 Economies and Diseconomies of Scale
		2.5 Software Engineering Datasets and Their Distribution
			2.5.1 Wedge-Shaped Datasets
			2.5.2 Homogeneous Datasets
		2.6 Productivity Models: Explicit and Implicit Variables
		2.7 A Single and Universal Catch-All Multidimensional Model or Multiple Simpler Models?
			2.7.1 Models Built from Available Data
			2.7.2 Models Built on Opinions on Cost Drivers
			2.7.3 Multiple Models with Coexisting Economies and Diseconomies of Scale
		Exercises
		Term Assignments
	Chapter 3 Project Scenarios, Budgeting, and Contingency Planning
		3.1 Introduction
		3.2 Project Scenarios for Estimation Purposes
		3.3 Probability of Underestimation and Contingency Funds
		3.4 A Contingency Example for a Single Project
		3.5 Managing Contingency Funds at the Portfolio Level
		3.6 Managerial Prerogatives: An Example in the AGILE Context
		3.7 Summary
		Further Reading: A Simulation for Budgeting at the Portfolio Level
		Exercises
		Term Assignments
Part II Estimation Process: What Must be Verified?
	Chapter 4 What Must be Verified in an Estimation Process: An Overview
		4.1 Introduction
		4.2 Verification of the Direct Inputs to An Estimation Process
			4.2.1 Identification of the Estimation Inputs
			4.2.2 Documenting the Quality of These Inputs
		4.3 Verification of the Productivity Model
			4.3.1 In-House Productivity Models
			4.3.2 Externally Provided Models
		4.4 Verification of the Adjustment Phase
		4.5 Verification of the Budgeting Phase
		4.6 Re-Estimation and Continuous Improvement to the Full Estimation Process
		Further Reading: The Estimation Verification Report
		Exercises
		Term Assignments
	Chapter 5 Verification of the Dataset Used to Build the Models
		5.1 Introduction
		5.2 Verification of DIRECT Inputs
			5.2.1 Verification of the Data Definitions and Data Quality
			5.2.2 Importance of the Verification of the Measurement Scale Type
		5.3 Graphical Analysis - One-Dimensional
		5.4 Analysis of the Distribution of the Input Variables
			5.4.1 Identification of a Normal (Gaussian) Distribution
			5.4.2 Identification of Outliers: One-Dimensional Representation
			5.4.3 Log Transformation
		5.5 Graphical Analysis - Two-Dimensional
		5.6 Size Inputs Derived from a Conversion Formula
		5.7 Summary
		Further Reading: Measurement and Quantification
		Exercises
		Term Assignments
		Exercises-Further Reading Section
		Term Assignments-Further Reading Section
	Chapter 6 Verification of Productivity Models
		6.1 Introduction
		6.2 Criteria Describing the Relationships Across Variables
			6.2.1 Simple Criteria
			6.2.2 Practical Interpretation of Criteria Values
			6.2.3 More Advanced Criteria
		6.3 Verification of the Assumptions of the Models
			6.3.1 Three Key Conditions Often Required
			6.3.2 Sample Size
		6.4 Evaluation of Models by Their Own Builders
		6.5 Models Already Built-Should You Trust Them?
			6.5.1 Independent Evaluations: Small-Scale Replication Studies
			6.5.2 Large-Scale Replication Studies
		6.6 Lessons Learned: Distinct Models by Size Range
			6.6.1 In Practice, Which is the Better Model?
		6.7 Summary
		Exercises
		Term Assignments
	Chapter 7 Verification of the Adjustment Phase
		7.1 Introduction
		7.2 Adjustment Phase in the Estimation Process
			7.2.1 Adjusting the Estimation Ranges
			7.2.2 The Adjustment Phase in the Decision-Making Process: Identifying Scenarios for Managers
		7.3 The Bundled Approach in Current Practices
			7.3.1 Overall Approach
			7.3.2 Detailed Approach for Combining the Impact of Multiple Cost Drivers in Current Models
			7.3.3 Selecting and Categorizing Each Adjustment: The Transformation of Nominal Scale Cost Drivers into Numbers
		7.4 Cost Drivers as Estimation Submodels!
			7.4.1 Cost Drivers as Step Functions
			7.4.2 Step Function Estimation Submodels with Unknown Error Ranges
		7.5 Uncertainty and Error Propagation
			7.5.1 Error Propagation in Mathematical Formulas
			7.5.2 The Relevance of Error Propagation in Models
		Exercises
		Term Assignments
Part III Building Estimation Models: Data Collection and Analysis
	Chapter 8 Data Collection and Industry Standards: The ISBSG Repository
		8.1 Introduction: Data Collection Requirements
		8.2 The International Software Benchmarking Standards Group
			8.2.1 The ISBSG Organization
			8.2.2 The ISBSG Repository
		8.3 ISBSG Data Collection Procedures
			8.3.1 The Data Collection Questionnaire
			8.3.2 ISBSG Data Definitions
		8.4 Completed ISBSG Individual Project Benchmarking Reports: Some Examples
		8.5 Preparing to Use the ISBSG Repository
			8.5.1 ISBSG Data Extract
			8.5.2 Data Preparation: Quality of the Data Collected
			8.5.3 Missing Data: An Example with Effort Data
		Further Reading 1: Benchmarking Types
		Further Reading 2: Detailed Structure of the ISBSG Data Extract
		Exercises
		Term Assignments
	Chapter 9 Building and Evaluating Single Variable Models
		9.1 Introduction
		9.2 Modestly, One Variable at a Time
			9.2.1 The Key Independent Variable: Software Size
			9.2.2 Analysis of the Work-Effort Relationship in a Sample
		9.3 Data Preparation
			9.3.1 Descriptive Analysis
			9.3.2 Identifying Relevant Samples and Outliers
		9.4 Analysis of the Quality and Constraints of Models
			9.4.1 Small Projects
			9.4.2 Larger Projects
			9.4.3 Implication for Practitioners
		9.5 Other Models by Programming Language
		9.6 Summary
		Exercises
		Term Assignments
	Chapter 10 Building Models with Categorical Variables
		10.1 Introduction
		10.2 The Available Dataset
		10.3 Initial Model with a Single Independent Variable
			10.3.1 Simple Linear Regression Model with Functional Size Only
			10.3.2 Nonlinear Regression Models with Functional Size
		10.4 Regression Models with Two Independent Variables
			10.4.1 Multiple Regression Models with Two Independent Quantitative Variables
			10.4.2 Multiple Regression Models with a Categorical Variable: Project Difficulty
			10.4.3 The Interaction of Independent Variables
		Exercises
		Term Assignments
	Chapter 11 Contribution of Productivity Extremes in Estimation
		11.1 Introduction
		11.2 Identification of Productivity Extremes
		11.3 Investigation of Productivity Extremes
			11.3.1 Projects with Very Low Unit Effort
			11.3.2 Projects with Very High Unit Effort
		11.4 Lessons Learned for Estimation Purposes
		Exercises
		Term Assignments
	Chapter 12 Multiple Models from a Single Dataset
		12.1 Introduction
		12.2 Low and High Sensitivity to Functional Size Increases: Multiple Models
		12.3 The Empirical Study
			12.3.1 Context
			12.3.2 Data Collection Procedures
			12.3.3 Data Quality Controls
		12.4 Descriptive Analysis
			12.4.1 Project Characteristics
			12.4.2 Documentation Quality and Its Impact on Functional Size Quality
			12.4.3 Unit Effort (in Hours)
		12.5 Productivity Analysis
			12.5.1 Single Model with the Full Dataset
			12.5.2 Model of the Least Productive Projects
			12.5.3 Model of the Most Productive Projects
		12.6 External Benchmarking with the ISBSG Repository
			12.6.1 Project Selection Criteria and Samples
			12.6.2 External Benchmarking Analysis
			12.6.3 Further Considerations
		12.7 Identification of the Adjustment Factors for Model Selection
			12.7.1 Projects with the Highest Productivity (i.e., the Lowest Unit Effort)
			12.7.2 Lessons Learned
		Exercises
		Term Assignments
	Chapter 13 Re-Estimation: A Recovery Effort Model
		13.1 Introduction
		13.2 The Need for Re-Estimation and Related Issues
		13.3 The Recovery Effort Model
			13.3.1 Key Concepts
			13.3.2 Ramp-Up Process Losses
		13.4 A Recovery Model When a Re-Estimation Need is Recognized at Time T>0
			13.4.1 Summary of Recovery Variables
			13.4.2 A Mathematical Model of a Recovery Course in Re-Estimation
			13.4.3 Probability of Underestimation -p(u)
			13.4.4 Probability of Acknowledging the Underestimation on a Given Month -p(t)
		Exercises
		Term Assignments
References
Index
EULA