Software Measurement and Estimation: A Practical Approach / Edition 1 available in Hardcover
An effective, quantitative approach for estimating and managing software projects How many people do I need? When will the quality be good enough for commercial sale? Can this really be done in two weeks? Rather than relying on instinct, the authors of Software Measurement and Estimation offer a new, tested approach that includes the quantitative tools, data, and knowledge needed to make sound estimations. The text begins with the foundations of measurement, identifies the appropriate metrics, and then focuses on techniques and tools for estimating the effort needed to reach a given level of quality and performance for a software project. All the factors that impact estimations are thoroughly examined, giving you the tools needed to regularly adjust and improve your estimations to complete a project on time, within budget, and at an expected level of quality. This text includes several features that have proven to be successful in making the material accessible and easy to master: * Simple, straightforward style and logical presentation and organization enables you to build a solid foundation of theory and techniques to tackle complex estimations * Examples, provided throughout the text, illustrate how to use theory to solve real-world problems * Projects, included in each chapter, enable you to apply your newfound knowledge and skills * Techniques for effective communication of quantitative data help you convey your findings and recommendations to peers and management Software Measurement and Estimation: A Practical Approach allows practicing software engineers and managers to better estimate, manage, and effectively communicate the plans and progress of their software projects. With its classroom-tested features, this is an excellent textbook for advanced undergraduate-level and graduate students in computer science and software engineering. An Instructor Support FTP site is available from the Wiley editorial department.
About the Author
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.
Table of Contents
Chapter 1 Side Bar.
2. What to Measure.
2.1 Method 1: The Goal Question Metrics Approach.
2.2 Extension to GQM: Metrics Mechanism is Important.
2.3 Method 2: Decision Maker Model.
2.4 Method 3: Standards Driven Metrics.
2.5 What to Measure is a Function of Time.
3. Fundamentals of Measurement.
3.1 Initial Measurement Exercise.
3.2 The Challenge of Measurement.
3.3 Measurement Models.
3.3.1 Text Models.
3.3.2 Diagrammatic Models.
3.3.3 Algorithmic Models.
3.3.4 Model Examples: Response Time.
3.3.5 The Pantometric Paradigm - How to Measure Anything.
3.4 Meta-Model for Metrics.
3.5 The Power of Measurement.
3.6 Measurement Theory.
3.6.1 Introduction to Measurement Theory.
3.6.2 Measurement Scales.
3.6.3 Measures of Central Tendency and Variability.
126.96.36.199 Measures of Central Tendency.
188.8.131.52 Measures of Variability.
3.6.4 Validity and Reliability of Measurement.
3.6.5 Measurement Error.
3.7 Accuracy versus Precision and the Limits of Software Measurement.
4. Measuring the Size of Software.
4.1 Physical Measurements of Software.
4.1.1 Measuring Lines of Code.
184.108.40.206 Code Counting Checklists.
4.1.2 Language Productivity Factor.
4.1.3 Counting Reused and Refactored Code.
4.1.4 Counting Non-Procedural Code Length.
4.1.5 Measuring the Length of Specifications and Design.
4.2 Measuring Functionality.
4.2.1 Function Points.
220.127.116.11 Counting Function Points.
4.2.2 Function Point Counting Exercise.
4.2.3 Converting Function Points to Physical Size.
4.2.4 Converting Function Points to Effort.
4.2.5 Other Function Point Engineering Rules.
4.2.6 Function Point Pros and Cons.
4.3 Feature Points.
4.4 Size Summary.
4.5 Size Exercises.
4.6 Theater Tickets Project.
5. Measuring Complexity.
5.1 Structural Complexity.
5.1.1 Size as a Complexity Measure.
18.104.22.168 System Size and Complexity.
22.214.171.124 Module Size and Complexity.
5.1.2 Cyclomatic Complexity.
5.1.3 Halstead's Metrics.
5.1.4 Information Flow Metrics.
5.1.5 System Complexity.
126.96.36.199 Maintainability Index.
188.8.131.52 The Agresti-Card System Complexity Metric.
5.1.6 Object-Oriented Design Metrics.
5.1.7 Structural Complexity Summary.
5.2 Conceptual Complexity.
5.3 Computational Complexity.
5.4 Complexity Metrics Summary.
5.5 Complexity Exercises.
6. Estimating Effort.
6.1 Effort Estimation - Where are we?.
6.2 Software Estimation Methodologies and Models.
6.2.1 Expert Estimation.
184.108.40.206 Work and Activity Decomposition.
220.127.116.11 System Decomposition.
18.104.22.168 The Delphi Methods.
6.2.2 Using Benchmark Size Data.
22.214.171.124 Lines of Code Benchmark Data.
126.96.36.199 Function Point Benchmark Data.
6.2.3 Estimation by Analogy.
188.8.131.52 Traditional Analogy Approach.
184.108.40.206 Analogy Summary.
6.2.4 Proxy Point Estimation Methods.
220.127.116.11 Meta-Model for Effort Estimation.
18.104.22.168 Function Points.
22.214.171.124.1 COSMIC Function Points.
126.96.36.199 Object Points.
188.8.131.52 Use Case Sizing Methodologies.
184.108.40.206.1 Use Case Points Methodology.
220.127.116.11.2 Example: Use Case Point Methodology Example: Home Security System.
18.104.22.168.3 Use Case Point Methodology Effectiveness.
6.2.5 Custom Models.
6.2.6 Algorithmic Models.
22.214.171.124 Manual Models.
126.96.36.199 Estimating Project Duration.
188.8.131.52 Tool Based Models.
6.3 Combining Estimates.
6.4 Estimating Issues.
6.4.1 Targets vs. Estimates.
6.4.2 The Limitations of Estimation - Why?.
6.4.3 Estimate Uncertainties.
6.5 Estimating Early and Often.
6.6 Estimation Summary.
6.7 Estimation Problems.
6.8 Estimation Project - Theater Tickets.
7. In Praise of Defects: Defects and Defect Metrics.
7.1 Why study and measure defects?.
7.2 Faults vs. failures.
7.3 Defect Dynamics and Behaviors.
7.3.1 Defect Arrival Rates.
7.3.2 Defects vs. Effort.
7.3.3 Defects vs. Staffing.
7.3.4 Defect Arrival Rates vs. Code Production Rate.
7.3.5 Defect Density vs. Module Complexity.
7.3.6 Defect Density vs. System Size.
7.4 Defect Projection Techniques and Models.
7.4.1 Dynamic Defect Models.
184.108.40.206 Rayleigh Models.
220.127.116.11 Exponential and S-Curves Arrival Distribution Models.
18.104.22.168 Empirical Data and Recommendations for Dynamic Models.
7.4.2 Static Defect Models.
22.214.171.124 Defect Insertion and Removal Model.
126.96.36.199 Defect Removal Efficiency - A Key Metric.
188.8.131.52 Static Defect Model Tools.
7.5 Additional Defect Benchmark Data.
7.5.1 Defect Data By Application Domain.
7.5.2 Cumulative Defect Removal Efficiency (DRE) Benchmark.
7.5.3 SEI Levels and Defect Relationships.
7.5.4 Latent Defects.
7.5.5 Other Defects Benchmarks and a Few Recommendations+.
7.6 Cost Effectiveness of Defect Removal by Phase.
7.7 Defining and Using Simple Defect Metrics: An example.
7.8 Some Paradoxical Patterns for Customer Reported Defects.
7.9 Defect Summary.
7.12 Answers to the initial questions.
8. Software Reliability Measurement and Prediction.
8.1 Why study and measure software reliability?.
8.2 What is reliability?.
8.3 Faults and failures.
8.4 Failure Severity Classes.
8.5 Failure Intensity.
8.6 The Cost of Reliability.
8.7 Software Reliability Theory.
8.7.1 Uniform and Random Distributions.
8.7.2 The probability of failure during a time interval.
8.7.3 F(t) - The Probability of Failure by time t.
8.7.4 R(t) - The Reliability Function.
8.7.5 Reliability Theory Summarized.
8.8 Reliability Models.
8.8.1 Types of Models.
8.8.2 Predicting Number of Defects Remaining.
8.8.3 Reliability Growth Models.
8.8.4 Model Summary.
8.9 Failure Arrival Rates.
8.9.1 Predicting Failure Arrival Rates Using Historical Data.
8.9.2 Engineering Rules for MTTF.
8.9.3 Musa's Algorithm.
8.9.4 Operational Profile Testing.
8.9.5 Predicting Reliability Summary.
8.10 But when do I ship?.
8.11 System Configurations: Probability and Reliability.
8.12 Answers to Initial Question.
8.13 Reliability Summary.
8.14 Reliability Exercises.
8.15 Reliability Project.
9. Response Time and Availability.
9.1 Response Time Measurements.
9.2.1 Availability Factors.
9.2.2 Outage Scope.
9.2.3 Complexities in Measuring Availability.
9.2.4 Software Rejuvenation.
184.108.40.206 Software Aging.
220.127.116.11 Classification of Faults.
18.104.22.168 Software Rejuvenation Techniques.
22.214.171.124 Impact of Rejuvenation on Availability.
10. Measuring Progress.
10.1 Project Milestones.
10.2 Code Integration.
10.3 Testing Progress.
10.4 Defects Discovery and Closure.
10.4.1 Defect Discovery.
10.4.2 Defect Closure.
10.5 Process Effectiveness.
11.1 The "O" Word.
11.2 Defining Outsourcing.
11.3 Risks Management and Outsourcing.
11.4 Metrics and the Contract.
Chapter 11 Sidebar.
12. Financial Measures for the Software Engineer.
12.1 It's All About the Green.
12.2 Financial Concepts.
12.3 Building the Business Case.
12.3.1 Understanding Costs.
126.96.36.199 Overhead Costs.
188.8.131.52 Risk Costs.
184.108.40.206.1 Identifying Risk.
220.127.116.11.2 Assessing Risks.
18.104.22.168.3 Planning for Risk.
22.214.171.124.4 Monitoring Risk.
126.96.36.199 Capital versus Expense.
12.3.2 Understanding Benefits.
12.3.3 Business Case Metrics.
188.8.131.52 Return on Investment.
184.108.40.206 Pay-Back Period.
220.127.116.11 Cost/Benefit Ratio.
18.104.22.168 Profit & Loss Statement.
22.214.171.124 Cash Flow.
126.96.36.199 Expected Value.
12.4 Living the Business Case.
13.1 What is Benchmarking.
13.2 Why Benchmark.
13.3 What to Benchmark.
13.4 Identifying and Obtaining a Benchmark.
13.5 Collecting Actual Data.
13.6 Taking Action.
13.7 Current Benchmarks.
14. Presenting Metrics Effectively to Management.
14.1 Decide on the Metrics.
14.2 Draw the Picture.
14.3 Create a Dashboard.
14.4 Drilling for Information.
14.5 Example for the Big Cheese.
14.6 Evolving Metrics.
What People are Saying About This
"It is a good and welcome addition to any technical library…and for anyone…who needs to manage software, either development or acquisition." (ACM Software Engineering Notes, January 2007)
"…an excellent book on this important area of software development." (CHOICE, December 2006)
"The authors offer software engineers and project managers a new, tested approach that provides the quantitative tools, data, and knowledge needed to make sound estimations." (IEEE Computer Magazine, August 2006)
"Corporations should advise both their project managers and their software teams to study methods introduced in this worthy text." (Kybernetes, Vol.36 Issue 1)