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Learning a Metric for Code Readability
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Learning a Metric for Code Readability  
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Abstract—In this paper, we explore the concept of code readability and investigate its relation to software quality. With data collected from 120 human annotators, we derive associations between a simple set of local code features and human notions of readability. Using those features, we construct an automated readability measure and show that it can be 80% effective, and better than a human on average, at predicting readability judgments. Furthermore, we show that this metric correlates strongly with three measures of software quality: code changes, automated defect reports, and defect log messages. We measure these correlations on over 2.2 million lines of code, as well as longitudinally, over many releases of select projects. Finally, we discuss the implications of this study on programming language design and engineering practice. For example, our data suggests that comments, in of themselves, are less important than simple blank lines to local judgments,of readability. Index Terms—software readability, program understanding, machine learning, software maintenance, code metrics, FindBugs F
Journal: IEEE Transactions on Software Engineering - TSE , vol. 36, no. 4, pp. 546-558, 2010
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