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Cited by 558

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Deep learning code fragments for code clone detection

White, Martin, Tufano, Michele, Vendome, Christopher, Poshyvanyk, Denys·2016·Proceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering
DOI 10.1145/2970276.2970326
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Abstract

Code clone detection is an important problem for software maintenance and evolution. Many approaches consider either structure or identifiers, but none of the existing detection techniques model both sources of information. These techniques also depend on generic, handcrafted features to represent code fragments. We introduce learning-based detection techniques where everything for representing terms and fragments in source code is mined from the repository. Our code analysis supports a framework, which relies on deep learning, for automatically linking patterns mined at the lexical level with patterns mined at the syntactic level. We evaluated our novel learning-based approach for code clone detection with respect to feasibility from the point of view of software maintainers. We sampled and manually evaluated 398 file- and 480 method-level pairs across eight real-world Java systems; 93% of the file- and method-level samples were evaluated to be true positives. Among the true positives, we found pairs mapping to all four clone types. We compared our approach to a traditional structure-oriented technique and found that our learning-based approach detected clones that were either undetected or suboptimally reported by the prominent tool Deckard. Our results affirm that our learning-based approach is suitable for clone detection and a tenable technique for researchers.

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Metadata

Authors
White, M., Tufano, M., Vendome, C., & Poshyvanyk, D.
Venue
Proceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering
Publisher
ACM
Published
2016-08-25
Pages
87-98
DOI
10.1145/2970276.2970326
OpenAlex
W2511803001
Cited by
558
References
8
FWCI
42.74
Type
conference paper
Language
en
Open access
closed
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)

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