A Metric for Code Readability
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This document presents a metric for measuring software readability. It hypothesizes that using a simple set of local code features, an accurate model of readability can be derived from human judgments of readability. The document outlines acquiring human readability judgments, extracting a predictive model from those judgments, evaluating the model's performance, and correlating readability with external notions of software quality and the software lifecycle.
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Features
We choose “local” code features
Line length
Length of identifier names
Comment density
Blank lines
Presence of numbers
[and 20 others]](https://image.slidesharecdn.com/isstareadability-101029005401-phpapp01/85/A-Metric-for-Code-Readability-14-320.jpg)
A Metric for Code Readability
- 1. A Metric for Software Readability Ray Buse ∙ Westley Weimer ISSTA 2008
- 2. 2 Readability “The quality that enables the observer to correctly perceive the message” Metrics for Natural Language Flesch-Kincaid Grade Level Gunning-Fog Index SMOG Index Automated Readability Index
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- 5. 5 Readability and Software Code maintenance = 70% of lifecycle cost. And most of maintenance effort is spent reading code! But do we have any way to gain some level of assurance in code readability?
- 6. 6 Hypothesis Employing a simple set of local features, we can derive, from a set of human judgments, an accurate model of readability for code. To what extent do humans agree on code readability? We know readability is important, but can we create a predictive model of it? What could such a model teach us?
- 7. 7 Outline Acquiring Human Readability Judgments Extracting a Model Model Performance Correlation with External Notions of Software Quality Readability and the Software Lifecycle
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- 11. 11 Scoring Data
- 13. 13 Setup
- 14. 14 Features We choose “local” code features Line length Length of identifier names Comment density Blank lines Presence of numbers [and 20 others]
- 16. 16 External Notions of Quality
- 19. 19 Conclusions We can automatically judge readability about as well as the “average” human can This notion of readability shows significant correlation with: Version Changes The output of a bug finder Self-reported program maturity We may also learn more about software readability by looking at the predictive power of our model’s features