Learning a Metric for Code Readability

By Raymond P. L. Buse, Westley Weimer. IEEE Transactions on Software Engineering, 2010

426 citations recorded by the source corpus.

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Learning natural coding conventions (2014, background): “tia publish guidelines for others, like C [8, 34]. High quality identiﬁer names lie at the heart of software engineering [6, 16, 20, 24, 42, 66, 69, 54]; they drive code readability and comprehension [12, 19, 20, 41, 44, 68]. According to Deißenböck and Pizka [17], identiﬁers represent the majority ”
Deep Just-In-Time Inconsistency Detection Between Comments and Source Code (2020, background): “Comments serve as a critical communication medium for developers, facilitating program comprehension and code maintenance tasks (Buse and Weimer 2010; de Souza, An-quetil, and de Oliveira 2005).”
Learning to rank code examples for code search engines (2017, methodology): “As indicated by Buse and Weimer (2010), lines of code and the average number of identifiers per line can be used to predict code readability as one of the quality aspects of code examples.”
Decoding the Representation of Code in the Brain: An fMRI Study of Code Review and Expertise (2017, background): “, [57] from [9]); we hope that the same will happen here.”
A Survey on Large Language Models for Code Generation (2024, neutral): “The results revealed that the most successful LLM can achieve up to 47% of the benchmarks.”