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…

Add the Complexity Dimension

You now have the data to trace the spatial movements of programmers within the code. In Chapter 2, ​Code as a Crime Scene​, we pointed out the importance of combining that data with a complexity dimension. Let’s see where the complexity is hiding in this code.

Get Complexity by Lines of Code

We’re going to use lines of code as a proxy for software complexity. Lines of code is a terrible metric, but the other ones are just as bad. (See the research by Herraiz and Hassan in Making Software [OW10] for a comparison of complexity metrics.) Using lines of code at least gives us some advantages:

  • It’s fast and simple. More elaborate metrics need to understand the language they’re processing. That means they need to parse the code, which may take some time. Lines of code is a fast way to get the same approximation of complexity.

  • It’s language-neutral. Language neutrality is the main reason I prefer lines of code. In today’s polyglot systems, sophisticated metrics lose their meaning. As we start to parse individual language constructs, we lose the opportunity for cross-language comparisons. For example, web applications often combine HTML, CSS, and JavaScript in addition to a server-side technology, such as Java, C#, or Clojure. A language-neutral metric lets us get a holistic picture of all these parts, no matter what language they’re written in.

We can always turn to language-specific techniques later to get more details on hotspots. Similarly, we can use any metric to represent complexity. For now, let’s summarize the lines of code in our system.

Counting Lines with cloc

Many tools count lines of code. My favorite is cloc. It’s free and easy to use. You can get a copy of cloc on SourceForge.[11]

With cloc installed, let’s put it to work:

​ 
prompt>​ cloc ./ --unix --by-file --csv --quiet​
​ 
​ 
language,filename,blank,comment,code
​ 
Clojure,./src/code_maat/analysis/logical_coupling.clj,23,14,145
​ 
Clojure,./test/code_maat/end_to_end/scenario_tests.clj,23,19,117
​ 
Clojure,./src/code_maat/analysis/churn.clj,14,11,99
​ 
Clojure,./src/code_maat/app/app.clj,13,6,94
​ 
Clojure,./test/code_maat/analysis/logical_coupling_test.clj,15,5,89
​ 
...

Here we told cloc to count all files in the code-maat directory. We also specified that we want statistics --by-file (the alternative is a summary) and --csv output. As you can see in the following figure, cloc does a good job of detecting the programming language the code is written in.

images/Chp3_ClocSample.png

Based on language, cloc separates lines containing comments from real code. We don’t want blank lines or comments in our analysis.