Rethinking Productivity in Software Engineering.

• Author: Sadowski, Caitlin.
• TeilnehmendeR: Zimmermann, Thomas.
• Place / Publishing House: Berkeley, CA : : Apress L. P.,, 2019.; ©2019.
• Year of Publication: 2019
• Edition: 1st ed.
• Language: English
• Physical Description: 1 online resource (320 pages)

Table of Contents

• Intro
• Table of Contents
• About the Editors
• Acknowledgments
• Introduction
• Part I: Measuring Productivity: No Silver Bullet
• Chapter 1: The Mythical 10x Programmer
• Some Work Time Variability Data
• Insisting on Homogeneity
• Deciding What We Even Mean
• Uninsisting on Homogeneity
• Questioning the Base Population
• It's Not Only About Development Effort
• Are Slower Programmers Just More Careful?
• Secondary Factors Can Be Important
• The Productivity Definition Revisited
• How Would Real People Work?
• So What?
• Key Ideas
• References
• Chapter 2: No Single Metric Captures Productivity
• What's Wrong with Measuring Individual Performers?
• Why Do People Want to Measure Developer Productivity?
• What's Inherently Wrong with a Single Productivity Metric?
• Productivity Is Broad
• Flattening/Combining Components of a Single Aspect Is Challenging
• Confounding Factors
• What Do We Do Instead at Google?
• Key Ideas
• References
• Chapter 3: Why We Should Not Measure Productivity
• Unintended Consequences
• Explaining Productivity
• Dealing with Change
• Managers as Measurers
• Key Ideas
• Part II: Introduction to Productivity
• Chapter 4: Defining Productivity in Software Engineering
• A Short History of Software Productivity
• Terminology in the General Literature
• Productivity
• Profitability
• Performance
• Efficiency and Effectiveness
• Influence of Quality
• An Integrated Definition of Software Productivity
• Summary
• Key Ideas
• Acknowledgements
• References
• Chapter 5: A Software Development Productivity Framework
• Productivity Dimensions in Software Development
• Velocity
• Quality
• Satisfaction
• Lenses
• The Productivity Framework in Action: Articulating Goals, Questions, and Metrics
• Example 1: Improving Productivity Through an Intervention.
• Productivity Goal 1: Improve Productivity at the Individual and Team Levels Through the Introduction of a New Continuous Integration System
• Example 2: Understanding How Meetings Impact Productivity
• Productivity Goal 2: Develop an Understanding of How Meetings May Impact Productivity
• Caveats
• Key Ideas
• References
• Chapter 6: Individual, Team, Organization, and Market: Four Lenses of Productivity
• The Individual
• The Team
• The Organization
• The Market
• Full-Spectrum Productivity
• Key Ideas
• References
• Chapter 7: Software Productivity Through the Lens of Knowledge Work
• A Brief History of Knowledge Work
• Techniques for Measuring Productivity
• Outcome-Oriented Techniques
• Process-Oriented Techniques
• People-Oriented Techniques
• Multi-oriented Techniques
• Drivers That Influence Productivity
• Software Developers vs. Knowledge Workers: Similar or Different?
• Summary
• Key Ideas
• References
• Part III: The Context of Productivity
• Chapter 8: Factors That Influence Productivity: A Checklist
• Introduction
• A Brief History of Productivity Factors Research
• The List of Technical Factors
• Product Factors
• Process Factors
• Development Environment
• The List of Soft Factors
• Corporate Culture
• Team Culture
• Individual Skills and Experiences
• Work Environment
• Project
• Summary
• Key Ideas
• Acknowledgments
• Appendix: Review Design
• References
• Chapter 9: How Do Interruptions Affect Productivity?
• Introduction
• Controlled Experiments
• What Is the Aim of an Experiment?
• A Typical Interruptions Experiment
• How Is Disruptiveness of an Interruption Measured?
• Interruptions Cause Errors
• Moving Controlled Experiments Out of the Lab
• Summary: Controlled Experiments
• Cognitive Models
• What Are Cognitive Models?.
• What Can Cognitive Models Predict About the Impact of Interruptions on Productivity?
• Summary: Cognitive Models
• Observational Studies
• Observational Studies of the Workplace
• Benefits and Detriments of Interruptions
• Stress, Individual Differences, and Interruptions
• Productivity
• Strategies for Dealing with Interruptions
• Summary: Observational Studies
• Key Insights
• Key Ideas
• Acknowledgments
• References
• Chapter 10: Happiness and the  Productivity of Software Engineers
• Why the Industry Should Strive for Happy Developers
• What Is Happiness, and How Do We Measure It?
• Scientific Grounds of Happy and Productive Developers
• How Happy Are Software Developers?
• What Makes Developers Unhappy?
• What Happens When Developers Are Happy (or Unhappy)?
• Cognitive Performance
• Flow
• Motivation and Withdrawal
• Happiness and Unhappiness, and How They Relate to the Productivity of Developers
• Are Happy Developers More Productive?
• Potential Impacts of Happiness on Other Outcomes
• What Does the Future Hold?
• Further Reading
• Key Ideas
• References
• Chapter 11: Dark Agile: Perceiving People As Assets, Not Humans
• Revisiting the Agile Manifesto
• Agile in Global Outsourcing Setups
• Tracking Work to Increase Productivity
• Daily Stand-Up Meeting to Monitor Productivity
• Stressful Work Environment
• Cost of Productivity
• Open Questions for Productivity in Software Engineering
• Key Ideas
• Acknowledgments
• References
• Part IV: Measuring Productivity in Practice
• Chapter 12: Developers' Diverging Perceptions of Productivity
• Quantifying Productivity: Measuring vs. Perceptions
• Studying Software Developers' Productivity Perceptions
• The Cost of Context Switching
• A Productive Workday in a Developer's Life
• Developers Expect Different Measures for Quantifying Productivity.
• Characterizing Software Developers by Perceptions of Productivity
• Opportunities for Improving Developer Productivity
• Key Ideas
• References
• Chapter 13: Human-Centered Methods to Boost Productivity
• Key Ideas
• References
• Chapter 14: Using Biometric Sensors to Measure Productivity
• Operationalizing Productivity for Measurement
• What the Eye Says About Focus
• Observing Attention with EEG
• Measuring Rumination
• Moving Forward
• Key Ideas
• References
• Chapter 15: How Team Awareness Influences Perceptions of Developer Productivity
• Introduction
• Awareness and Productivity
• Enabling Awareness in Collaborative Software Development
• Aggregating Awareness Information into Numbers
• Aggregating Awareness Information into Text
• Rethinking Productivity and Team Awareness
• Key ideas
• References
• Chapter 16: Software Engineering Dashboards: Types, Risks, and Future
• Introduction
• Dashboards in Software Engineering
• Developer Activity
• Team Performance
• Project Monitoring and Performance
• Community Health
• Summary
• Risks of Using Dashboards
• Rethinking Dashboards in Software Engineering
• Key Ideas
• References
• Chapter 17: The COSMIC Method for  Measuring the Work-Output Component of Productivity
• Measurement of Functional Size
• The COSMIC Method
• Discussion of the COSMIC Model
• Correlation of COSMIC Sizes with Development Effort
• Automated COSMIC Size Measurement
• Conclusions
• Key Ideas
• References
• Chapter 18: Benchmarking: Comparing Apples to Apples
• Introduction
• The Use of Standards
• Functional Size Measurement
• Reasons for Benchmarking
• A Standard Way of Benchmarking
• Normalizing
• Sources of Benchmark Data
• ISBSG Repository
• Internal Benchmark Data Repository
• Benchmarking in Practice
• False Incentives
• Summary
• Key Ideas
• Further Reading.
• Part V: Best Practices for Productivity
• Chapter 19: Removing Software Development Waste to Improve Productivity
• Introduction
• Taxonomy of Software Development Waste
• Building the Wrong Feature or Product
• Mismanaging the Backlog
• Rework
• Unnecessarily Complicated or Complex Solutions
• Extraneous Cognitive Load
• Psychological Distress
• Knowledge Loss
• Waiting/Multitasking
• Ineffective Communication
• Additional Wastes in Pre-agile Projects
• Discussion
• Not All Problems Are Wastes
• Reducing Waste
• Conclusion
• Key Ideas
• References
• Chapter 20: Organizational Maturity: The Elephant Affecting Productivity
• Background
• The Process Maturity Framework
• The Impact of Maturity on Productivity and Quality
• Updating Maturity Practices for an Agile-DevOps Environment
• Summary
• Key Ideas
• References
• Chapter 21: Does Pair Programming Pay Off?
• Introduction: Highly Productive Programming
• Studying Pair Programming
• Software Development As Knowledge Work
• What Actually Matters in Industrial Pair Programming
• Constellation A: System Knowledge Advantage
• Constellation B: Collective System Knowledge Gap
• Constellation C: Complementary Knowledge
• So, Again: Does Pair Programming Pay Off?
• Key Ideas
• References
• Chapter 22: Fitbit for Developers: Self-Monitoring at Work
• Self-Monitoring to Quantify Our Lives
• Self-Monitoring Software Developers' Work
• Supporting Various Individual Needs Through Personalization
• Self-Reporting Increases Developers' Awareness About Efficiency
• Retrospection About Work Increases Developers' Self-Awareness
• Actionable Insights Foster Productive Behavior Changes
• Increasing Team Awareness and Solving Privacy Concerns
• Fostering Sustainable Behaviors at Work
• Key Ideas
• References
• Chapter 23: Reducing Interruptions at Work with FlowLight.
• The Cost of Interruptions at Work.