Many claims are made about how certain tools, technologies, and practices improve software development. But which claims are verifiable, and which are merely wishful thinking? In this book, leading thinkers such as Steve McConnell, Barry Boehm, and Barbara Kitchenham offer essays that uncover the truth and unmask myths commonly held among the software development community. Their insights may surprise you.
- Are some programmers really ten times more productive than others?
- Does writing tests first help you develop better code faster?
- Can code metrics predict the number of bugs in a piece of software?
- Do design patterns actually make better software?
- What effect does personality have on pair programming?
- What matters more: how far apart people are geographically, or how far apart they are in the org chart?
Victor R. Basili
Jo E. Hannay
Ahmed E. Hassan
Kim Sebastian Herzig
Thomas J. Ostrand
Elaine J. Weyuker
Michele A. Whitecraft
Wendy M. Williams
About The Author:
Andy Oram is an editor at O'Reilly Media, a highly respected book publisher and technology information provider. An employee of the company since 1992, Andy currently specializes in free software and open source technologies. His work for O'Reilly includes the first books ever published commercially in the United States on Linux, and the 2001 title Peer-to-Peer. His modest programming and system administration skills are mostly self-taught.
View Andy Oram's full profile page.
Greg Wilson has worked on high-performance scientific computing, data visualization, and computer security, and is currently project lead at Software Carpentry (http://software-carpentry.org). Greg has a Ph.D. in Computer Science from the University of Edinburgh, and has written and edited several technical and children's books, including "Beautiful Code" (O'Reilly, 2007).
View Greg Wilson's full profile page.
Table Of Contents:
General Principles of Searching For and Using Evidence
Chapter 1 The Quest for Convincing Evidence
- In the Beginning
- The State of Evidence Today
- Change We Can Believe In
- The Effect of Context
- Looking Toward the Future
Chapter 2 Credibility, or Why Should I Insist on Being Convinced?
- How Evidence Turns Up in Software Engineering
- Credibility and Relevance
- Aggregating Evidence
- Types of Evidence and Their Strengths and Weaknesses
- Society, Culture, Software Engineering, and You
Chapter 3 What We Can Learn from Systematic Reviews
- An Overview of Systematic Reviews
- The Strengths and Weaknesses of Systematic Reviews
- Systematic Reviews in Software Engineering
Chapter 4 Understanding Software Engineering Through Qualitative Methods
- What Are Qualitative Methods?
- Reading Qualitative Research
- Using Qualitative Methods in Practice
- Generalizing from Qualitative Results
- Qualitative Methods Are Systematic
Chapter 5 Learning Through Application: The Maturing of the QIP in the SEL
- What Makes Software Engineering Uniquely Hard to Research
- A Realistic Approach to Empirical Research
- The NASA Software Engineering Laboratory: A Vibrant Testbed for Empirical Research
- The Quality Improvement Paradigm
Chapter 6 Personality, Intelligence, and Expertise: Impacts on Software Development
- How to Recognize Good Programmers
- Individual or Environment
- Concluding Remarks
Chapter 7 Why Is It So Hard to Learn to Program?
- Do Students Have Difficulty Learning to Program?
- What Do People Understand Naturally About Programming?
- Making the Tools Better by Shifting to Visual Programming
- Contextualizing for Motivation
- Conclusion: A Fledgling Field
Chapter 8 Beyond Lines of Code: Do We Need More Complexity Metrics?
- Surveying Software
- Measuring the Source Code
- A Sample Measurement
- Statistical Analysis
- Some Comments on the Statistical Methodology
- So Do We Need More Complexity Metrics?
Specific Topics in Software Engineering
Chapter 9 An Automated Fault Prediction System
- Fault Distribution
- Characteristics of Faulty Files
- Overview of the Prediction Model
- Replication and Variations of the Prediction Model
- Building a Tool
- The Warning Label
Chapter 10 Architecting: How Much and When?
- Does the Cost of Fixing Software Increase over the Project Life Cycle?
- How Much Architecting Is Enough?
- Using What We Can Learn from Cost-to-Fix Data About the Value of Architecting
- So How Much Architecting Is Enough?
- Does the Architecting Need to Be Done Up Front?
Chapter 11 Conway’s Corollary
- Conway’s Law
- Coordination, Congruence, and Productivity
- Organizational Complexity Within Microsoft
- Chapels in the Bazaar of Open Source Software
Chapter 12 How Effective Is Test-Driven Development?
- The TDD Pill—What Is It?
- Summary of Clinical TDD Trials
- The Effectiveness of TDD
- Enforcing Correct TDD Dosage in Trials
- Cautions and Side Effects
- General References
- Clinical TDD Trial References
Chapter 13 Why Aren’t More Women in Computer Science?
- Why So Few Women?
- Should We Care?
Chapter 14 Two Comparisons of Programming Languages
- A Language Shoot-Out over a Peculiar Search Algorithm
- Plat_Forms: Web Development Technologies and Cultures
- So What?
Chapter 15 Quality Wars: Open Source Versus Proprietary Software
- Past Skirmishes
- The Battlefield
- Into the Battle
- Outcome and Aftermath
- Acknowledgments and Disclosure of Interest
Chapter 16 Code Talkers
- A Day in the Life of a Programmer
- What Is All This Talk About?
- A Model for Thinking About Communication
Chapter 17 Pair Programming
- A History of Pair Programming
- Pair Programming in an Industrial Setting
- Pair Programming in an Educational Setting
- Distributed Pair Programming
- Lessons Learned
Chapter 18 Modern Code Review
- Common Sense
- A Developer Does a Little Code Review
- Group Dynamics
Chapter 19 A Communal Workshop or Doors That Close?
- Doors That Close
- A Communal Workshop
- Work Patterns
- One More Thing…
Chapter 20 Identifying and Managing Dependencies in Global Software Development
- Why Is Coordination a Challenge in GSD?
- Dependencies and Their Socio-Technical Duality
- From Research to Practice
- Future Directions
Chapter 21 How Effective Is Modularization?
- The Systems
- What Is a Change?
- What Is a Module?
- The Results
- Threats to Validity
Chapter 22 The Evidence for Design Patterns
- Design Pattern Examples
- Why Might Design Patterns Work?
- The First Experiment: Testing Pattern Documentation
- The Second Experiment: Comparing Pattern Solutions to Simpler Ones
- The Third Experiment: Patterns in Team Communication
- Lessons Learned
Chapter 23 Evidence-Based Failure Prediction
- Code Coverage
- Code Churn
- Code Complexity
- Code Dependencies
- People and Organizational Measures
- Integrated Approach for Prediction of Failures
Chapter 24 The Art of Collecting Bug Reports
- Good and Bad Bug Reports
- What Makes a Good Bug Report?
- Survey Results
- Evidence for an Information Mismatch
- Problems with Bug Reports
- The Value of Duplicate Bug Reports
- Not All Bug Reports Get Fixed
Chapter 25 Where Do Most Software Flaws Come From?
- Studying Software Flaws
- Context of the Study
- Phase 1: Overall Survey
- Phase 2: Design/Code Fault Survey
- What Should You Believe About These Results?
- What Have We Learned?
Chapter 26 Novice Professionals: Recent Graduates in a First Software Engineering Job
- Study Methodology
- Software Development Task
- Strengths and Weaknesses of Novice Software Developers
- Misconceptions That Hinder Learning
- Reflecting on Pedagogy
- Implications for Change
Chapter 27 Mining Your Own Evidence
- What Is There to Mine?
- Designing a Study
- A Mining Primer
- Where to Go from Here
Chapter 28 Copy-Paste as a Principled Engineering Tool
- An Example of Code Cloning
- Detecting Clones in Software
- Investigating the Practice of Code Cloning
- Our Study
Chapter 29 How Usable Are Your APIs?
- Why Is It Important to Study API Usability?
- First Attempts at Studying API Usability
- If At First You Don’t Succeed...
- Adapting to Different Work Styles
Chapter 30 What Does 10x Mean? Measuring Variations in Programmer Productivity
- Individual Productivity Variation in Software Development
- Issues in Measuring Productivity of Individual Programmers
- Team Productivity Variation in Software Development