Table of Contents for
Your Code as a Crime Scene

Version ebook / Retour

Cover image for bash Cookbook, 2nd Edition Your Code as a Crime Scene by Adam Tornhill Published by Pragmatic Bookshelf, 2015
  1. Title Page
  2. Your Code as a Crime Scene
  3. Your Code as a Crime Scene
  4. For the Best Reading Experience...
  5. Table of Contents
  6. Early praise for Your Code as a Crime Scene
  7. Foreword by Michael Feathers
  8. Acknowledgments
  9. Chapter 1: Welcome!
  10. About This Book
  11. Optimize for Understanding
  12. How to Read This Book
  13. Toward a New Approach
  14. Get Your Investigative Tools
  15. Part 1: Evolving Software
  16. Chapter 2: Code as a Crime Scene
  17. Meet the Problems of Scale
  18. Get a Crash Course in Offender Profiling
  19. Profiling the Ripper
  20. Apply Geographical Offender Profiling to Code
  21. Learn from the Spatial Movement of Programmers
  22. Find Your Own Hotspots
  23. Chapter 3: Creating an Offender Profile
  24. Mining Evolutionary Data
  25. Automated Mining with Code Maat
  26. Add the Complexity Dimension
  27. Merge Complexity and Effort
  28. Limitations of the Hotspot Criteria
  29. Use Hotspots as a Guide
  30. Dig Deeper
  31. Chapter 4: Analyze Hotspots in Large-Scale Systems
  32. Analyze a Large Codebase
  33. Visualize Hotspots
  34. Explore the Visualization
  35. Study the Distribution of Hotspots
  36. Differentiate Between True Problems and False Positives
  37. Chapter 5: Judge Hotspots with the Power of Names
  38. Know the Cognitive Advantages of Good Names
  39. Investigate a Hotspot by Its Name
  40. Understand the Limitations of Heuristics
  41. Chapter 6: Calculate Complexity Trends from Your Code’s Shape
  42. Complexity by the Visual Shape of Programs
  43. Learn About the Negative Space in Code
  44. Analyze Complexity Trends in Hotspots
  45. Evaluate the Growth Patterns
  46. From Individual Hotspots to Architectures
  47. Part 2: Dissect Your Architecture
  48. Chapter 7: Treat Your Code As a Cooperative Witness
  49. Know How Your Brain Deceives You
  50. Learn the Modus Operandi of a Code Change
  51. Use Temporal Coupling to Reduce Bias
  52. Prepare to Analyze Temporal Coupling
  53. Chapter 8: Detect Architectural Decay
  54. Support Your Redesigns with Data
  55. Analyze Temporal Coupling
  56. Catch Architectural Decay
  57. React to Structural Trends
  58. Scale to System Architectures
  59. Chapter 9: Build a Safety Net for Your Architecture
  60. Know What’s in an Architecture
  61. Analyze the Evolution on a System Level
  62. Differentiate Between the Level of Tests
  63. Create a Safety Net for Your Automated Tests
  64. Know the Costs of Automation Gone Wrong
  65. Chapter 10: Use Beauty as a Guiding Principle
  66. Learn Why Attractiveness Matters
  67. Write Beautiful Code
  68. Avoid Surprises in Your Architecture
  69. Analyze Layered Architectures
  70. Find Surprising Change Patterns
  71. Expand Your Analyses
  72. Part 3: Master the Social Aspects of Code
  73. Chapter 11: Norms, Groups, and False Serial Killers
  74. Learn Why the Right People Don’t Speak Up
  75. Understand Pluralistic Ignorance
  76. Witness Groupthink in Action
  77. Discover Your Team’s Modus Operandi
  78. Mine Organizational Metrics from Code
  79. Chapter 12: Discover Organizational Metrics in Your Codebase
  80. Let’s Work in the Communication Business
  81. Find the Social Problems of Scale
  82. Measure Temporal Coupling over Organizational Boundaries
  83. Evaluate Communication Costs
  84. Take It Step by Step
  85. Chapter 13: Build a Knowledge Map of Your System
  86. Know Your Knowledge Distribution
  87. Grow Your Mental Maps
  88. Investigate Knowledge in the Scala Repository
  89. Visualize Knowledge Loss
  90. Get More Details with Code Churn
  91. Chapter 14: Dive Deeper with Code Churn
  92. Cure the Disease, Not the Symptoms
  93. Discover Your Process Loss from Code
  94. Investigate the Disposal Sites of Killers and Code
  95. Predict Defects
  96. Time to Move On
  97. Chapter 15: Toward the Future
  98. Let Your Questions Guide Your Analysis
  99. Take Other Approaches
  100. Let’s Look into the Future
  101. Write to Evolve
  102. Appendix 1: Refactoring Hotspots
  103. Refactor Guided by Names
  104. Bibliography
  105. You May Be Interested In…

Mining Evolutionary Data

Version-control data is our software-development equivalent to spatial movement in geographical profiling. A version-control system—such as Subversion, Git, or Mercurial—records the steps each developer took. I focus on Git in my examples, but you can find an overview of how to mine data from other systems in the Code Maat documentation.[8]

The first codebase we’ll study is Code Maat. That’s right—we’ll use our analysis tool to analyze the tool itself. I’m a Lisp programmer—we love circular stuff like that.

To follow along with the examples, you need to clone the Code Maat repository[9] so that you have the complete source tree on your computer:

​ 
prompt>​ git clone https://github.com/adamtornhill/code-maat.git​
​ 
Cloning into 'code-maat'...
​ 
remote: Reusing existing pack: 1092, done.
​ 
remote: Total 1092 (delta 0), reused 0 (delta 0)
​ 
Receiving objects: 100% (1092/1092), 365.75 KiB | 271.00 KiB/s, done.
​ 
Resolving deltas: 100% (537/537), done.
​ 
Checking connectivity... done.
​ 
prompt>

Once the clone command completes, you’ll find a local code-maat directory with the source code. Move into that directory:

​ 
prompt>​cd code-maat​

Turn Back Time

Code Maat is still under development, and some of the worst issues we spot here will probably be fixed by the time you read this. So we are going to pretend it’s still 2013. That’s fine—the digital world lets us easily travel back to less gracious times:

​ 
prompt>​ git checkout `git rev-list -n 1 --before="2013-11-01" master`​
​ 
...​​
​ 
HEAD is now at d804759... Documented tree map visualizations

The git command is a bit tricky because it does two things: it fetches the revision on the specified date and checks out that revision. Other version-control systems provide similar rollback mechanisms.

Your local copy of Code Maat should now look as the code did back in 2013.

Investigate the Git Log

Git lets us inspect historical commits by its log command. To get the level of detail we need, we use the --numstat flag:

​ 
prompt>​ git log --numstat​

This command will output a detailed log, as shown in the figure.

images/Chp3_GitLogSketch.png

The sample output contains a lot of valuable information. In the following chapters, we’ll get several opportunities to inspect it in depth. For now, we’ll limit the analysis to the changed modules. We see that the oldest commit involved changes to six files, while the next one only modified churn.clj.

Verify Your Intuitions

images/aside-icons/info.png

Human intuition is wonderful for making quick decisions. The quality of those decisions, however, is not always wonderful.

Expert intuition can lead to high-quality decisions. The problem is that we don’t know up front whether this time is one of those expert intuitions. Intuition is an automatic, unconscious process, and like all automatic mental processes, it’s sensitive to cognitive and social biases. Factors in your surroundings or in the specific situation can influence your judgment. Most of the time you aren’t aware of that influence. (You’ll see some examples in Chapter 11, ​Norms, Groups, and False Serial Killers​.) For example, we may be notoriously bad at evaluating past decisions due to hindsight bias. That’s why it’s important to verify your intuitive ideas with supporting data.

From a practical perspective, we need guiding techniques like the ones presented here because intuition doesn’t scale—especially not in complex areas, such as a large-scale software project that is constantly changing.