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. Making Software will open your eyes and help you choose the tools and technologies that are right for you.
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.nAre some programmers really ten times more productive than others?nDoes writing tests first help you develop better code faster?nCan code metrics predict the number of bugs in a piece of software?nDo design patterns actually make better software?nWhat effect does personality have on pair programming?nWhat matters more: how far apart people are geographically, or how far apart they are in the org chart?nnContributors include:nnJorge ArandannTom BallnnVictor R. BasilinnAndrew BegelnnChristian BirdnnBarry BoehmnnMarcelo CataldonnSteven ClarkennJason CohennnRobert DeLinennMadeline DiepnnHakan ErdogmusnnMichael GodfreynnMark GuzdialnnJo E. HannaynnAhmed E. HassannnIsrael HerraiznnKim Sebastian HerzignnCory KapsernnBarbara KitchenhamnnAndrew KonnLucas LaymannnSteve McConnellnnTim MenziesnnGail MurphynnNachi NagappannnThomas J. OstrandnnDewayne PerrynnMarian PetrennLutz PrecheltnnRahul PremrajnnForrest ShullnnBeth SimonnnDiomidis SpinellisnnNeil ThomasnnWalter TichynnBurak TurhannnElaine J. WeyukernnMichele A. WhitecraftnnLaurie WilliamsnnWendy M. WilliamsAndreas ZellernnThomas Zimmermann
Andy Oram is an editor at O'Reilly & Associates, specializing in books on Linux and programming. Most recently, he edited Peer-to-Peer: Harnessing the Power of Disruptive Technologies.
Über den Autor
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 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).
Inhaltsverzeichnis
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;
Klappentext
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
