NDLI: Deep learning code fragments for code clone detection

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Deep learning code fragments for code clone detection

Content Provider	ACM Digital Library
Author	Poshyvanyk, Denys Tufano, Michele White, Martin Vendome, Christopher
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. .
Starting Page	87
Ending Page	98
Page Count	12
File Format	PDF
ISBN	9781450338455
DOI	10.1145/2970276.2970326
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-08-25
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Deep learning Neural networks Code clone detection Machine learning Abstract syntax trees Language models
Content Type	Text
Resource Type	Article

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