Making Software

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?

Contributors include:

Jorge Aranda

Tom Ball

Victor R. Basili

Andrew Begel

Christian Bird

Barry Boehm

Marcelo Cataldo

Steven Clarke

Jason Cohen

Robert DeLine

Madeline Diep

Hakan Erdogmus

Michael Godfrey

Mark Guzdial

Jo E. Hannay

Ahmed E. Hassan

Israel Herraiz

Kim Sebastian Herzig

Cory Kapser

Barbara Kitchenham

Andrew Ko

Lucas Layman

Steve McConnell

Tim Menzies

Gail Murphy

Nachi Nagappan

Thomas J. Ostrand

Dewayne Perry

Marian Petre

Lutz Prechelt

Rahul Premraj

Forrest Shull

Beth Simon

Diomidis Spinellis

Neil Thomas

Walter Tichy

Burak Turhan

Elaine J. Weyuker

Michele A. Whitecraft

Laurie Williams

Wendy M. Williams

Andreas Zeller

Thomas Zimmermann

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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. Greg Wilson holds a Ph.D. in Computer Science from the University of Edinburgh, and has worked on high-performance scientific computing, data visualization, and computer security. He is the author of Data Crunching and Practical Parallel Programming (MIT Press, 1995), a contributing editor at Doctor Dobb's Journal, and an adjunct professor in Computer Science at the University of Toronto.

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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 Fixi...

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