Improving Cross-Language Code Clone Detection via Code Representation Learning and Graph Neural Networks

Code clone detection is an important aspect of software development and maintenance. The extensive research in this domain has helped reduce the complexity and increase the robustness of source code, thereby assisting bug detection tools. However, the majority of the clone detection literature is co...

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Vydané v:	IEEE transactions on software engineering Ročník 49; číslo 11; s. 4846 - 4868
Hlavní autori:	Mehrotra, Nikita, Sharma, Akash, Jindal, Anmol, Purandare, Rahul
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.11.2023 IEEE Computer Society
Predmet:	abstract syntax trees Cloning Codes cross-language code clone detection Deep learning Fragments Graph neural networks graph-based neural networks Graphical representations Java Machine learning Neural networks Program representation learning Programming languages Semantics Software development Source code Source coding Syntactics Task analysis
ISSN:	0098-5589, 1939-3520
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Abstract	Code clone detection is an important aspect of software development and maintenance. The extensive research in this domain has helped reduce the complexity and increase the robustness of source code, thereby assisting bug detection tools. However, the majority of the clone detection literature is confined to a single language. With the increasing prevalence of cross-platform applications, functionality replication across multiple languages is common, resulting in code fragments having similar functionality but belonging to different languages. Since such clones are syntactically unrelated, single language clone detection tools are not applicable in their case. In this article, we propose a semi-supervised deep learning-based tool Rubhus , capable of detecting clones across different programming languages. Rubhus uses the control and data flow enriched abstract syntax trees (ASTs) of code fragments to leverage their syntactic and structural information and then applies graph neural networks (GNNs) to extract this information for the task of clone detection. We demonstrate the effectiveness of our proposed system through experiments conducted over datasets consisting of Java, C, and Python programs and evaluate its performance in terms of precision, recall, and F1 score. Our results indicate that Rubhus outperforms the state-of-the-art cross-language clone detection tools.
AbstractList	Code clone detection is an important aspect of software development and maintenance. The extensive research in this domain has helped reduce the complexity and increase the robustness of source code, thereby assisting bug detection tools. However, the majority of the clone detection literature is confined to a single language. With the increasing prevalence of cross-platform applications, functionality replication across multiple languages is common, resulting in code fragments having similar functionality but belonging to different languages. Since such clones are syntactically unrelated, single language clone detection tools are not applicable in their case. In this article, we propose a semi-supervised deep learning-based tool Rubhus , capable of detecting clones across different programming languages. Rubhus uses the control and data flow enriched abstract syntax trees (ASTs) of code fragments to leverage their syntactic and structural information and then applies graph neural networks (GNNs) to extract this information for the task of clone detection. We demonstrate the effectiveness of our proposed system through experiments conducted over datasets consisting of Java, C, and Python programs and evaluate its performance in terms of precision, recall, and F1 score. Our results indicate that Rubhus outperforms the state-of-the-art cross-language clone detection tools.
Author	Purandare, Rahul Jindal, Anmol Mehrotra, Nikita Sharma, Akash
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SubjectTerms	abstract syntax trees Cloning Codes cross-language code clone detection Deep learning Fragments Graph neural networks graph-based neural networks Graphical representations Java Machine learning Neural networks Program representation learning Programming languages Semantics Software development Source code Source coding Syntactics Task analysis
Title	Improving Cross-Language Code Clone Detection via Code Representation Learning and Graph Neural Networks
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