Software Project Estimation The Fundamentals for Providing High Quality Information to Decision Makers 1st Edition by Alain Abran ISBN 978-1118954089 1118954084

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Product details: 

ISBN 10: 1118954084

ISBN 13:  978-1118954089

Author: Alain Abran

This book introduces theoretical concepts to explain the fundamentals of the design and evaluation of software estimation models. It provides software professionals with vital information on the best software management software out there.

• End-of-chapter exercises
• Over 100 figures illustrating the concepts presented throughout the book
• Examples incorporated with industry data

Table of contents: 

Part One: Understanding the Estimation Process
1. The Estimation Process: Phases and Roles

• Introduction

• Generic Approaches in Estimation Models: Judgment or Engineering?

  • Practitioner’s Approach: Judgment and Craftsmanship

  • Engineering Approach: Modest–One Variable at a Time

• Overview of Software Project Estimation and Current Practices

  • Overview of an Estimation Process

  • Poor Estimation Practices

  • Examples of Poor Estimation Practices

  • The Reality: A Tally of Failures

• Levels of Uncertainty in an Estimation Process

  • The Cone of Uncertainty

  • Uncertainty in a Productivity Model

• Productivity Models

• The Estimation Process

  • The Context of the Estimation Process

  • The Foundation: The Productivity Model

  • The Full Estimation Process

• Budgeting and Estimating: Roles and Responsibilities

  • Project Budgeting: Levels of Responsibility

  • The Estimator

  • The Manager (Decision-Taker and Overseer)

• Pricing Strategies

  • Customers-Suppliers: The Risk Transfer Game in Estimation

• Summary – Estimating Process, Roles, and Responsibilities

• Exercises

• Term Assignments

2. Engineering and Economics Concepts for Understanding Software Process Performance

• Introduction: The Production (Development) Process

• The Engineering (and Management) Perspective on a Production Process

• Simple Quantitative Process Models

  • Productivity Ratio

  • Unit Effort (or Unit Cost) Ratio

  • Averages

  • Linear and Non-Linear Models

• Quantitative Models and Economics Concepts

  • Fixed and Variable Costs

  • Economies and Diseconomies of Scale

• Software Engineering Datasets and Their Distribution

  • Wedge-Shaped Datasets

  • Homogeneous Datasets

• Productivity Models: Explicit and Implicit Variables

• A Single and Universal Catch-All Multidimensional Model or Multiple Simpler Models?

  • Models Built from Available Data

  • Models Built on Opinions on Cost Drivers

  • Multiple Models with Coexisting Economies and Diseconomies of Scale

• Exercises

• Term Assignments

3. Project Scenarios, Budgeting, and Contingency Planning

• Introduction

• Project Scenarios for Estimation Purposes

• Probability of Underestimation and Contingency Funds

• A Contingency Example for a Single Project

• Managing Contingency Funds at the Portfolio Level

• Managerial Prerogatives: An Example in the AGILE Context

• Summary

• Further Reading: A Simulation for Budgeting at the Portfolio Level

• Exercises

• Term Assignments

Part Two: Estimation Process – What Must Be Verified?
4. What Must Be Verified in an Estimation Process: An Overview

• Introduction

• Verification of the Direct Inputs to an Estimation Process

  • Identification of the Estimation Inputs

  • Documenting the Quality of These Inputs

• Verification of the Productivity Model

  • In-House Productivity Models

  • Externally Provided Models

• Verification of the Adjustment Phase

• Verification of the Budgeting Phase

• Re-Estimation and Continuous Improvement to the Full Estimation Process

• Further Reading: The Estimation Verification Report

• Exercises

• Term Assignments

5. Verification of the Dataset Used to Build the Models

• Introduction

• Verification of Direct Inputs

  • Verification of the Data Definitions and Data Quality

  • Importance of the Verification of the Measurement Scale Type

• Graphical Analysis – One-Dimensional

• Analysis of the Distribution of the Input Variables

  • Identification of a Normal (Gaussian) Distribution

  • Identification of Outliers: One-Dimensional Representation

  • Log Transformation

• Graphical Analysis – Two-Dimensional

• Size Inputs Derived from a Conversion Formula

• Summary

• Further Reading: Measurement and Quantification

• Exercises

• Term Assignments

6. Verification of Productivity Models

• Introduction

• Criteria Describing the Relationships Across Variables

  • Simple Criteria

  • Practical Interpretation of Criteria Values

  • More Advanced Criteria

• Verification of the Assumptions of the Models

  • Three Key Conditions Often Required

  • Sample Size

• Evaluation of Models by Their Own Builders

• Models Already Built – Should You Trust Them?

  • Independent Evaluations: Small-Scale Replication Studies

  • Large-Scale Replication Studies

• Lessons Learned: Distinct Models by Size Range

  • In Practice, Which Is the Better Model?

• Summary

• Exercises

• Term Assignments

7. Verification of the Adjustment Phase

• Introduction

• Adjustment Phase in the Estimation Process

  • Adjusting the Estimation Ranges

  • The Adjustment Phase in the Decision-Making Process: Identifying Scenarios for Managers

• The Bundled Approach in Current Practices

  • Overall Approach

  • Detailed Approach for Combining the Impact of Multiple Cost Drivers

  • Selecting and Categorizing Each Adjustment

• Cost Drivers as Estimation Submodels

  • Cost Drivers as Step Functions

  • Step Function Estimation Submodels with Unknown Error Ranges

• Uncertainty and Error Propagation

  • Error Propagation in Mathematical Formulas

  • The Relevance of Error Propagation in Models

• Exercises

• Term Assignments

Part Three: Building Estimation Models – Data Collection and Analysis
8. Data Collection and Industry Standards: The ISBSG Repository

• Introduction: Data Collection Requirements

• The International Software Benchmarking Standards Group

  • The ISBSG Organization

  • The ISBSG Repository

• ISBSG Data Collection Procedures

  • The Data Collection Questionnaire

  • ISBSG Data Definitions

• Completed ISBSG Individual Project Benchmarking Reports: Examples

• Preparing to Use the ISBSG Repository

  • ISBSG Data Extract

  • Data Preparation: Quality of the Data Collected

  • 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

9. Building and Evaluating Single Variable Models

• Introduction

• Modestly, One Variable at a Time

  • The Key Independent Variable: Software Size

  • Analysis of the Work–Effort Relationship in a Sample

• Data Preparation

  • Descriptive Analysis

  • Identifying Relevant Samples and Outliers

• Analysis of the Quality and Constraints of Models

  • Small Projects

  • Larger Projects

  • Implication for Practitioners

• Other Models by Programming Language

• Summary

• Exercises

• Term Assignments

10. Building Models with Categorical Variables

• Introduction

• The Available Dataset

• Initial Model with a Single Independent Variable

  • Simple Linear Regression Model with Functional Size Only

  • Nonlinear Regression Models with Functional Size

• Regression Models with Two Independent Variables

  • Multiple Regression Models with Two Independent Quantitative Variables

  • Multiple Regression Models with a Categorical Variable: Project Difficulty

  • The Interaction of Independent Variables

• Exercises

• Term Assignments

11. Contribution of Productivity Extremes in Estimation

• Introduction

• Identification of Productivity Extremes

• Investigation of Productivity Extremes

  • Projects with Very Low Unit Effort

  • Projects with Very High Unit Effort

• Lessons Learned for Estimation Purposes

• Exercises

• Term Assignments

12. Multiple Models from a Single Dataset

• Introduction

• Low and High Sensitivity to Functional Size Increases: Multiple Models

• The Empirical Study

  • Context

  • Data Collection Procedures

  • Data Quality Controls

• Descriptive Analysis

  • Project Characteristics

  • Documentation Quality and Its Impact on Functional Size Quality

  • Unit Effort (in Hours)

• Productivity Analysis

  • Single Model with the Full Dataset

  • Model of the Least Productive Projects

  • Model of the Most Productive Projects

• External Benchmarking with the ISBSG Repository

  • Project Selection Criteria and Samples

  • External Benchmarking Analysis

  • Further Considerations

• Identification of the Adjustment Factors for Model Selection

  • Projects with the Highest Productivity

  • Lessons Learned

• Exercises

• Term Assignments

13. Re-Estimation: A Recovery Effort Model

• Introduction

• The Need for Re-Estimation and Related Issues

• The Recovery Effort Model

  • Key Concepts

  • Ramp-Up Process Losses

• A Recovery Model When a Re-Estimation Need Is Recognized

  • Summary of Recovery Variables

  • A Mathematical Model of a Recovery Course

  • Probability of Underestimation

  • Probability of Acknowledging the Underestimation

• Exercises

• Term Assignments

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Tags: Alain Abran, Software Project, The Fundamentals, High Qualit