Agile Metrics in Action: How To Measure and Improve Team Performance

by Christopher W. H. Davis

Overview

Summary

Agile Metrics in Action is a rich resource for agile teams that aim to use metrics to objectively measure performance. You'll learn how to gather data that really counts, along with how to effectively analyze and act upon the results.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the Book

The iterative nature of agile development is perfect for experience-based, continuous improvement. Tracking systems, test and build tools, source control, continuous integration, and other built-in parts of a project lifecycle throw off a wealth of data you can use to improve your products, processes, and teams. The question is, how to do it?

Agile Metrics in Action teaches you how. This practical book is a rich resource for an agile team that aims to use metrics to objectively measure performance. You'll learn how to gather the data that really count, along with how to effectively analyze and act upon the results. Along the way, you'll discover techniques all team members can use for better individual accountability and team performance.

Practices in this book will work with any development process or tool stack. For code-based examples, this book uses Groovy, Grails, and MongoDB.

What's Inside

• Use the data you generate every day from CI and Scrum
• Improve communication, productivity, transparency, and morale
• Objectively measure performance
• Make metrics a natural byproduct of your development process

About the Author

Christopher Davis has been a software engineer and team leader for over 15 years. He has led numerous teams to successful delivery using agile methodologies.

Table of Contents

PART 1 MEASURING AGILE TEAMS
1. Measuring agile performance
2. Observing a live project
PART 2 COLLECTING AND ANALYZING YOUR TEAM'S DATA
3. Trends and data from project-tracking systems
4. Trends and data from source control
5. Trends and data from CI and deployment servers
6. Data from your production systems
PART 3 APPLYING METRICS TO YOUR TEAMS, PROCESSES, AND SOFTWARE
7. Working with the data you're collecting: the sum of the parts
8. Measuring the technical quality of your software
9. Publishing metrics
10. Measuring your team against the agile principles

Product Details

ISBN-13:	9781617292484
Publisher:	Manning Publications Company
Publication date:	07/31/2015
Pages:	272
Product dimensions:	7.30(w) x 9.10(h) x 0.70(d)

About the Author

Christopher Davis has been a software engineer and team leader for over 15 years. He has led numerous teams to successful delivery using agile methodologies.

Table of Contents

Foreword xiii

Preface xv

Acknowledgments xvii

About this book xix

Part 1 Measuring Agile Teams 1

1 Measuring agile performance 3

1.1 Collect, measure, react, repeat-the feedback loop 4

What are metrics? 5

1.2 Why agile teams struggle with measurement 5

Problem: agile definitions of measurement are not straightforward 6

Problem: agile focuses on a product, not a project 7

Problem: data is all over the place without a unified view 8

1.3 What questions can metrics answer, and where do I get the data to answer them? 9

Project tracking 10

Source control 11

The build system 11

System monitoring 12

1.4 Analyzing what you have and what to do with the data 13

Figuring out what matters 14

Visualizing your data 14

1.5 Applying lessons learned 16

1.6 Taking ownership and measuring your team 16

Getting buy-in 17

Metric naysayers 18

1.7 Summary 19

2 Observing a live project 20

2.1 A typical agile project 20

How Blastamo Music used agile 21

2.2 A problem arises 21

2.3 Determining the right solution 22

2.4 Analyzing and presenting the data 26

Solving the problems 27

Visualizing the final product for leadership 28

2.5 Building on the system and improving their processes 31

Using data to improve what they do even day 32

2.6 Summary 33

Part 2 Collecting and Analyzing Your Team's Data 35

3 Trends and data from project-tracking systems 37

3.1 Typical agile measurements using PTS data 39

Burn down 39

Velocity 40

Cumulative flow 41

Lead time 42

Bug counts 42

3.2 Prepare for analysis; generate the richest set of data you can 44

Tip 1 Make sure everyone uses your PTS 45

Tip 2 Tag tasks with as much data as possible 46

Tip 3 Estimate how long you think your tasks will take 47

Tip 4 Clearly define when tasks are done 49

Tip 5 Clearly define when tasks are completed in a good way 50

3.3 Key project management metrics; spotting trends in data 52

Task volume 52

Bugs 53

Measuring task movement; recidivism and workflow 54

Sorting with tags and labels 55

3.4 Case study: identifying tech debt trending with project tracking data 57

3.5 Summary 60

4 Trends and data from source control 62

4.1 What is source control management? 63

4.2 Preparing for analysis: generate the richest set of data you can 64

Tip 1: Use distributed version control and pull requests 65

4.3 The data you'll be working with; what you can get from SCM 68

The data you can get from a DVCS 68

Data you can get from centralized SCM 71

What you can tell from SCM alone 71

4.4 Key SCM metrics: spotting trends in your data 77

Charting SCM activity 78

4.5 Case study: moving to the pull request workflow and incorporating quality engineering 79

4.6 Summary 82

5 Trends and data from CI and deployment servers 84

5.1 What is continuous development? 86

Continuous integration 86

Continuous delivery 88

Continuous testing 89

5.2 Preparing for analysis: generate the richest set of data you can 90

Set up a delivery pipeline 90

5.3 The data you'll be working with: what you can get from your CI APIs 91

The data you can get from your CI server 91

What you can tell from CI alone 96

5.4 Key CI metrics: spotting trends in your data 97

Getting CI data and adding it to your charts 97

5.5 Case study: measuring benefits of process change through CI data 101

5.6 Summary 105

6 Data from your production systems 107

6.1 Preparing for analysis: generating the richest set of data you can 109

Adding arbitrary metrics to your development cycle 110

Utilizing the features of your application performance monitoring system 113

Using logging best practices 115

Using social network interaction to connect with your consumers 116

6.2 The data you'll be working with: what you can get from your APM systems 118

Server health statistics 118

Consumer usage 120

Semantic logging analysis 120

Tools used to collect production system data 121

6.3 Case study: a team moves to DevOps and continuous delivery 122

6.4 Summary 124

Part 3 Applying Metrics to Your Teams, Processes, and Software 125

7 Working with the data you're collecting: the sum of the parts 127

7.1 Combining data points to create metrics 127

7.2 Using your data to define "good" 129

Turning subjectivity into objectivity 130

Working backward from good releases 132

7.3 How to create metrics 135

Step 1 Explore your data 136

Step 2 Break it down- determine what to track 139

Step 3 Create formulas around multiple data points to create metrics 140

7.4 Case study: creating and using a new metric to measure continuous release quality 144

7.5 Summary 153

8 Measuring the technical quality of your software 154

8.1 Preparing for analysis: setting up to measure your code 155

8.2 Measuring the NFRs through the code "ilities" 156

8.3 Measuring maintainability 158

MTTR and lead time 158

Adding SCM and build-data 162

Code coverage 164

Adding static code analysis 165

Adding more PTS data 167

8.4 Measuring usability 168

Reliability and availability 169

Security 171

8.5 Case study: finding anomalies in lead lime 173

8.6 Summary 176

9 Publishing metrics 177

9.1 The right data for the right audience 178

What to use on your team 180

What managers want to see 184

What executives care about 188

Using metrics to prove a point or effect change 189

9.2 Different publishing methods 191

Building dashboards 192

Using email 193

9.3 Case study: driving visibility toward a strategic goal 194

9.4 Summary 199

10 Measuring your team against the agile principles 201

10.1 Breaking the agile principles into measurable components 202

Aligning the principles with the delivery lifecycle 204

10.2 Three principles for effective software 205

Measuring effective software 206

10.3 Four principles for effective process 207

Measuring effective processes 208

10.4 Four principles for an effective team 210

Measuring an effective development team 210

10.5 One principle for effective requirements 213

Measuring effective requirements 213

10.6 Case study: a new agile team 215

10.7 Summary 219

Appendix A DIY analytics using ELK 221

Appendix B Collecting data from source systems with Grails 229

Index 319