Making Software

Preface; Organization of This Book; Conventions Used in This Book; Safari?? Books Online; Using Code Examples; How to Contact Us; General Principles of Searching For and Using Evidence; Chapter 1: The Quest for Convincing Evidence; 1.1 In the Beginning; 1.2 The State of Evidence Today; 1.3 Change We Can Believe In; 1.4 The Effect of Context; 1.5 Looking Toward the Future; 1.6 References; Chapter 2: Credibility, or Why Should I Insist on Being Convinced?; 2.1 How Evidence Turns Up in Software Engineering; 2.2 Credibility and Relevance; 2.3 Aggregating Evidence; 2.4 Types of Evidence and Their Strengths and Weaknesses; 2.5 Society, Culture, Software Engineering, and You; 2.6 Acknowledgments; 2.7 References; Chapter 3: What We Can Learn from Systematic Reviews; 3.1 An Overview of Systematic Reviews; 3.2 The Strengths and Weaknesses of Systematic Reviews; 3.3 Systematic Reviews in Software Engineering; 3.4 Conclusion; 3.5 References; Chapter 4: Understanding Software Engineering Through Qualitative Methods; 4.1 What Are Qualitative Methods?; 4.2 Reading Qualitative Research; 4.3 Using Qualitative Methods in Practice; 4.4 Generalizing from Qualitative Results; 4.5 Qualitative Methods Are Systematic; 4.6 References; Chapter 5: Learning Through Application: The Maturing of the QIP in the SEL; 5.1 What Makes Software Engineering Uniquely Hard to Research; 5.2 A Realistic Approach to Empirical Research; 5.3 The NASA Software Engineering Laboratory: A Vibrant Testbed for Empirical Research; 5.4 The Quality Improvement Paradigm; 5.5 Conclusion; 5.6 References; Chapter 6: Personality, Intelligence, and Expertise: Impacts on Software Development; 6.1 How to Recognize Good Programmers; 6.2 Individual or Environment; 6.3 Concluding Remarks; 6.4 References; Chapter 7: Why Is It So Hard to Learn to Program?; 7.1 Do Students Have Difficulty Learning to Program?; 7.2 What Do People Understand Naturally About Programming?; 7.3 Making the Tools Better by Shifting to Visual Programming; 7.4 Contextualizing for Motivation; 7.5 Conclusion: A Fledgling Field; 7.6 References; Chapter 8: Beyond Lines of Code: Do We Need More Complexity Metrics?; 8.1 Surveying Software; 8.2 Measuring the Source Code; 8.3 A Sample Measurement; 8.4 Statistical Analysis; 8.5 Some Comments on the Statistical Methodology; 8.6 So Do We Need More Complexity Metrics?; 8.7 References; Specific Topics in Software Engineering; Chapter 9: An Automated Fault Prediction System; 9.1 Fault Distribution; 9.2 Characteristics of Faulty Files; 9.3 Overview of the Prediction Model; 9.4 Replication and Variations of the Prediction Model; 9.5 Building a Tool; 9.6 The Warning Label; 9.7 References; Chapter 10: Architecting: How Much and When?; 10.1 Does the Cost of Fixing Software Increase over the Project Life Cycle?; 10.2 How Much Architecting Is Enough?; 10.3 Using What We Can Learn from Cost-to-Fix Data About the Value of Architecting; 10.4 So How Much Architecting Is Enough?; 10.5 Does the Architecting Need to Be Done Up Front?; 10.6 Conclusions; 10.7 References; Chapter 11: Conway???s Corollary; 11.1 Conway???s Law; 11.2 Coordination, Congruence, and Productivity; 11.3 Organizational Complexity Within Microsoft; 11.4 Chapels in the Bazaar of Open Source Software; 11.5 Conclusions; 11.6 References; Chapter 12: How Effective Is Test-Driven Development?; 12.1 The TDD Pill???What Is It?; 12.2 Summary of Clinical TDD Trials; 12.3 The Effectiveness of TDD; 12.4 Enforcing Correct TDD Dosage in Trials; 12.5 Cautions and Side Effects; 12.6 Conclusions; 12.7 Acknowledgments; 12.8 General References; 12.9 Clinical TDD Trial References; Chapter 13: Why Aren???t More Women in Computer Science?; 13.1 Why So Few Women?; 13.2 Should We Care?; 13.3 Conclusion; 13.4 References; Chapter 14: Two Comparisons of Programming Languages; 14.1 A Language Shoot-Out over a Peculiar Search Algorithm; 14.2 Plat_Forms: Web Development Technologies and Cultures; 14.3 So What?; 14.4 References; Chapter 15: Quality Wars: Open Source Versus Proprietary Software; 15.1 Past Skirmishes; 15.2 The Battlefield; 15.3 Into the Battle; 15.4 Outcome and Aftermath; 15.5 Acknowledgments and Disclosure of Interest; 15.6 References; Chapter 16: Code Talkers; 16.1 A Day in the Life of a Programmer; 16.2 What Is All This Talk About?; 16.3 A Model for Thinking About Communication; 16.4 References; Chapter 17: Pair Programming; 17.1 A History of Pair Programming; 17.2 Pair Programming in an Industrial Setting; 17.3 Pair Programming in an Educational Setting; 17.4 Distributed Pair Programming; 17.5 Challenges; 17.6 Lessons Learned; 17.7 Acknowledgments; 17.8 References; Chapter 18: Modern Code Review; 18.1 Common Sense; 18.2 A Developer Does a Little Code Review; 18.3 Group Dynamics; 18.4 Conclusion; 18.5 References; Chapter 19: A Communal Workshop or Doors That Close?; 19.1 Doors That Close; 19.2 A Communal Workshop; 19.3 Work Patterns; 19.4 One More Thing???; 19.5 References; Chapter 20: Identifying and Managing Dependencies in Global Software Development; 20.1 Why Is Coordination a Challenge in GSD?; 20.2 Dependencies and Their Socio-Technical Duality; 20.3 From Research to Practice; 20.4 Future Directions; 20.5 References; Chapter 21: How Effective Is Modularization?; 21.1 The Systems; 21.2 What Is a Change?; 21.3 What Is a Module?; 21.4 The Results; 21.5 Threats to Validity; 21.6 Summary; 21.7 References; Chapter 22: The Evidence for Design Patterns; 22.1 Design Pattern Examples; 22.2 Why Might Design Patterns Work?; 22.3 The First Experiment: Testing Pattern Documentation; 22.4 The Second Experiment: Comparing Pattern Solutions to Simpler Ones; 22.5 The Third Experiment: Patterns in Team Communication; 22.6 Lessons Learned; 22.7 Conclusions; 22.8 Acknowledgments; 22.9 References; Chapter 23: Evidence-Based Failure Prediction; 23.1 Introduction; 23.2 Code Coverage; 23.3 Code Churn; 23.4 Code Complexity; 23.5 Code Dependencies; 23.6 People and Organizational Measures; 23.7 Integrated Approach for Prediction of Failures; 23.8 Summary; 23.9 Acknowledgments; 23.10 References; Chapter 24: The Art of Collecting Bug Reports; 24.1 Good and Bad Bug Reports; 24.2 What Makes a Good Bug Report?; 24.3 Survey Results; 24.4 Evidence for an Information Mismatch; 24.5 Problems with Bug Reports; 24.6 The Value of Duplicate Bug Reports; 24.7 Not All Bug Reports Get Fixed; 24.8 Conclusions; 24.9 Acknowledgments; 24.10 References; Chapter 25: Where Do Most Software Flaws Come From?; 25.1 Studying Software Flaws; 25.2 Context of the Study; 25.3 Phase 1: Overall Survey; 25.4 Phase 2: Design/Code Fault Survey; 25.5 What Should You Believe About These Results?; 25.6 What Have We Learned?; 25.7 Acknowledgments; 25.8 References; Chapter 26: Novice Professionals: Recent Graduates in a First Software Engineering Job; 26.1 Study Methodology; 26.2 Software Development Task; 26.3 Strengths and Weaknesses of Novice Software Developers; 26.4 Reflections; 26.5 Misconceptions That Hinder Learning; 26.6 Reflecting on Pedagogy; 26.7 Implications for Change; 26.8 References; Chapter 27: Mining Your Own Evidence; 27.1 What Is There to Mine?; 27.2 Designing a Study; 27.3 A Mining Primer; 27.4 Where to Go from Here; 27.5 Acknowledgments; 27.6 References; Chapter 28: Copy-Paste as a Principled Engineering Tool; 28.1 An Example of Code Cloning; 28.2 Detecting Clones in Software; 28.3 Investigating the Practice of Code Cloning; 28.4 Our Study; 28.5 Conclusions; 28.6 References; Chapter 29: How Usable Are Your APIs?; 29.1 Why Is It Important to Study API Usability?; 29.2 First Attempts at Studying API Usability; 29.3 If At First You Don???t Succeed...; 29.4 Adapting to Different Work Styles; 29.5 Conclusion; 29.6 References; Chapter 30: What Does 10x Mean? Measuring Variations in Programmer Productivity; 30.1 Individual Productivity Variation in Software Development; 30.2 Issues in Measuring Productivity of Individual Programmers; 30.3 Team Productivity Variation in Software Development; 30.4 References; Contributors; Colophon;