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Cited by 376

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Learning natural coding conventions

Open access
Allamanis, Miltiadis, Barr, Earl T., Bird, Christian, Sutton, Charles·2014·Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering
DOI 10.1145/2635868.2635883
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Abstract

Every programmer has a characteristic style, ranging from preferences about identifier naming to preferences about object relationships and design patterns. Coding conventions define a consistent syntactic style, fostering readability and hence maintainability. When collaborating, programmers strive to obey a project’s coding conventions. However, one third of reviews of changes contain feedback about coding conventions, indicating that programmers do not always follow them and that project members care deeply about adherence. Unfortunately, programmers are often unaware of coding conventions because inferring them requires a global view, one that aggregates the many local decisions programmers make and identifies emergent consensus on style. We present NATURALIZE, a framework that learns the style of a codebase, and suggests revisions to improve stylistic consistency. NATURALIZE builds on recent work in applying statistical natural language processing to source code. We apply NATURALIZE to suggest natural identifier names and formatting conventions. We present four tools focused on ensuring natural code during development and release management, including code review. NATURALIZE achieves 94 % accuracy in its top suggestions for identifier names. We used NATURALIZE to generate 18 patches for 5 open source projects: 14 were accepted.

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Cited by

Metadata

Authors
Allamanis, M., Barr, E. T., Bird, C., & Sutton, C.
Venue
Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering
Publisher
ACM
Published
2014-11-11
Pages
281-293
DOI
10.1145/2635868.2635883
OpenAlex
W2140609933
Cited by
376
References
7
FWCI
25.08
Type
conference paper
Language
en
Open access
green
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)

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