Software Measurement and Estimation A Practical Approach

ISBN-10: 0471676225
ISBN-13: 9780471676225
Edition: 2006
List price: $120.00 Buy it from $59.19
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Description: Practical Estimation in Software Engineering is a practical guide to metrics and quantitative software estimation. The book begins with the foundations of measurement and metrics, and then focuses on techniques and tools for estimation of the  More...

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Book details

List price: $120.00
Copyright year: 2006
Publisher: IEEE Computer Society Press
Publication date: 6/5/2006
Binding: Hardcover
Pages: 280
Size: 6.25" wide x 9.25" long x 0.75" tall
Weight: 1.100
Language: English

Practical Estimation in Software Engineering is a practical guide to metrics and quantitative software estimation. The book begins with the foundations of measurement and metrics, and then focuses on techniques and tools for estimation of the required effort and the resulting quality of a software project. The factors that impact the estimations are also examined, so that readers can continually improve their estimations, based upon adjusting the characteristics of a project, to complete a project on-time, on budget, and with the required quality. The book includes numerous examples to illustrate how to use the tools and methodologies to solve real problems.

LINDA M. LAIRD is Adjunct Professor at Stevens Institute of Technology, where she teaches Quantitative Software Engineering. Dr. Laird has more than thirty years of experience building and managing systems for Lucent Technologies, AT&T, and Bell Laboratories.M. CAROL BRENNAN is former vice president and general manager of the Quality and Operations Center at Telcordia Technologies, where she was responsible for design, development, and implementation of the Telcordia quality management system. She has more than twenty-five years of experience in software design, development, testing, performance, maintenance, and customer support as well as quality strategy, policy, and implementation.

Acknowledgments
Introduction
Objective
Approach
Motivation
Summary
References
What to Measure
Method 1: The Goal Question Metrics Approach
Method 2: Decision Maker Model
Method 3: Standards Driven Metrics
Extension to GQM: Metrics Mechanism
What to Measure Is a Function of Time
Summary
Problems
Project
References
Measurement Fundamentals
Initial Measurement Exercise
The Challenge of Measurement
Measurement Models
Text Models
Diagrammatic Models
Algorithmic Models
Model Examples: Response Time
The Pantometric Paradigm: How to Measure Anything
Meta-Model for Metrics
The Power of Measurement
Measurement Theory
Introduction to Measurement Theory
Measurement Scales
Measures of Central Tendency and Variability
Measures of Central Tendency
Measures of Variability
Validity and Reliability of Measurement
Measurement Error
Accuracy Versus Precision and the Limits of Software Metrics
Summary
Problems
Projects
References
Measuring Size
Physical Measurements of Software
Measuring Lines of Code
Language Productivity Factor
Counting Reused and Refactored Code
Counting Nonprocedural Code Length
Measuring the Length of Specifications and Design
Measuring Functionality
Function Points
Counting Function Points
Function Point Example
Converting Function Points to Physical Size
Converting Function Points to Effort
Other Function Point Engineering Rules
Function Point Pros and Cons
Feature Points
Summary
Problems
Project
References
Measuring Complexity
Structural Complexity
Size as a Complexity Measure
System Size and Complexity
Module Size and Complexity
Cyclomatic Complexity
Halstead's Metrics
Information Flow Metrics
System Complexity
Maintainability Index
The Agresti-Card System Complexity Metric
Object-Oriented Design Metrics
Structural Complexity Summary
Conceptual Complexity
Computational Complexity
Summary
Problems
Projects
References
Estimating Effort
Effort Estimation: Where Are We?
Software Estimation Methodologies and Models
Expert Estimation
Work and Activity Decomposition
System Decomposition
The Delphi Methods
Using Benchmark Size Data
Lines of Code Benchmark Data
Function Point Benchmark Data
Estimation by Analogy
Traditional Analogy Approach
Analogy Summary
Proxy Point Estimation Methods
Meta-Model for Effort Estimation
Function Points
Object Points
Use Case Sizing Methodologies
Custom Models
Algorithmic Models
Manual Models
Estimating Project Duration
Tool-Based Models
Combining Estimates
Estimating Issues
Targets Versus Estimates
The Limitations of Estimation: Why?
Estimate Uncertainties
Estimating Early and Often
Summary
Problems
Projects
References
In Praise of Defects: Defects and Defect Metrics
Why Study and Measure Defects?
Faults Versus Failures
Defect Dynamics and Behaviors
Defect Arrival Rates
Defects Versus Effort
Defects Versus Staffing
Defect Arrival Rates Versus Code Production Rate
Defect Density Versus Module Complexity
Defect Density Versus System Size
Defect Projection Techniques and Models
Dynamic Defect Models
Rayleigh Models
Exponential and S-Curves Arrival Distribution Models
Empirical Data and Recommendations for Dynamic Models
Static Defect Models
Defect Insertion and Removal Model
Defect Removal Efficiency: A Key Metric
Static Defect Model Tools
Additional Defect Benchmark Data
Defect Data by Application Domain
Cumulative Defect Removal Efficiency (DRE) Benchmark
SEI Levels and Defect Relationships
Latent Defects
A Few Recommendations
Cost Effectiveness of Defect Removal by Phase
Defining and Using Simple Defect Metrics: An Example
Some Paradoxical Patterns for Customer Reported Defects
Answers to the Initial Questions
Summary
Problems
Projects
References
Software Reliability Measurement and Prediction
Why Study and Measure Software Reliability?
What Is Reliability?
Faults and Failures
Failure Severity Classes
Failure Intensity
The Cost of Reliability
Software Reliability Theory
Uniform and Random Distributions
The Probability of Failure During a Time Interval
F(t): The Probability of Failure by Time T
R(t): The Reliability Function
Reliability Theory Summarized
Reliability Models
Types of Models
Predicting Number of Defects Remaining
Failure Arrival Rates
Predicting Failure Arrival Rates Using Historical Data
Engineering Rules for MTTF
Musa's Algorithm
Operational Profile Testing
Predicting Reliability Summary
But When Do I Ship?
System Configurations: Probability and Reliability
Answers to Initial Question
Summary
Problems
Project
References
Response Time and Availability
Response Time Measurements
Availability
Availability Factors
Outage Scope
Complexities in Measuring Availability
Software Rejuvenation
Software Aging
Classification of Faults
Software Rejuvenation Techniques
Impact of Rejuvenation on Availability
Summary
Problems
Project
References
Measuring Progress
Project Milestones
Code Integration
Testing Progress
Defects Discovery and Closure
Defect Discovery
Defect Closure
Process Effectiveness
Summary
Problems
Project
References
Outsourcing
The "O" Word
Defining Outsourcing
Risk Management and Outsourcing
Metrics and the Contract
Summary
Problems
Projects
References
Financial Measures for the Software Engineer
It's All About the Green
Financial Concepts
Building the Business Case
Understanding Costs
Salaries
Overhead costs
Risk Costs
Capital Versus Expense
Understanding Benefits
Business Case Metrics
Return on Investment
Payback Period
Cost/Benefit Ratio
Profit and Loss Statement
Cash Flow
Expected Value
Living the Business Case
Summary
Problems
Projects
References
Benchmarking
What Is Benchmarking?
Why Benchmark?
What to Benchmark
Identifying and Obtaining a Benchmark
Collecting Actual Data
Taking Action
Current Benchmarks
Summary
Problems
Projects
References
Presenting Metrics Effectively to Management
Decide on the Metrics
Draw the Picture
Create a Dashboard
Drilling for Information
Example for the Big Cheese
Evolving Metrics
Summary
Problems
Project
Reference
Index

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