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In this paper, we explore the concept of code readability and investigate its relation to software quality. With data collected from 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 twodoi:10.1145/1390630.1390647 dblp:conf/issta/BuseW08 fatcat:pdd6fepgufbpdfcydvqxslsltu