Modeling functional similarity in source code with graph-based Siamese networks
Code clones are duplicate code fragments that share (nearly) similar syntax or semantics. Code clone detection plays an important role in software maintenance, code refactoring, and reuse. A substantial amount of research has been conducted in the past to detect clones. A majority of these approache...
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sg-smu-ink.sis_research-86612023-01-10T03:46:06Z Modeling functional similarity in source code with graph-based Siamese networks MEHROTRA, Nikita AGARWAL, Navdha GUPTA, Piyush ANAND, Saket LO, David PURANDARE, Rahul Code clones are duplicate code fragments that share (nearly) similar syntax or semantics. Code clone detection plays an important role in software maintenance, code refactoring, and reuse. A substantial amount of research has been conducted in the past to detect clones. A majority of these approaches use lexical and syntactic information to detect clones. However, only a few of them target semantic clones. Recently, motivated by the success of deep learning models in other fields, including natural language processing and computer vision, researchers have attempted to adopt deep learning techniques to detect code clones. These approaches use lexical information (tokens) and(or) syntactic structures like abstract syntax trees (ASTs) to detect code clones. However, they do not make sufficient use of the available structural and semantic information hence, limiting their capabilities. This paper addresses the problem of semantic code clone detection using program dependency graphs and geometric neural networks, leveraging the structured syntactic and semantic information. We have developed a prototype tool HOLMES, based on our novel approach and empirically evaluated it on popular code clone benchmarks. Our results show that HOLMES performs considerably better than the other state-of-the-art tool, TBCCD. We also evaluated HOLMES on unseen projects and performed cross dataset experiments to assess the generalizability of HOLMES. Our results affirm that HOLMES outperforms TBCCD since most of the pairs that HOLMES detected were either undetected or suboptimally reported by TBCCD. 2022-02-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/7658 info:doi/10.1109/TSE.2021.3105556 https://ink.library.smu.edu.sg/context/sis_research/article/8661/viewcontent/2011.11228.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Program representation learning Semantic code clones graph-based neural networks siamese neural networks program dependency graphs Graphics and Human Computer Interfaces OS and Networks Software Engineering |
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Program representation learning Semantic code clones graph-based neural networks siamese neural networks program dependency graphs Graphics and Human Computer Interfaces OS and Networks Software Engineering MEHROTRA, Nikita AGARWAL, Navdha GUPTA, Piyush ANAND, Saket LO, David PURANDARE, Rahul Modeling functional similarity in source code with graph-based Siamese networks |
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Code clones are duplicate code fragments that share (nearly) similar syntax or semantics. Code clone detection plays an important role in software maintenance, code refactoring, and reuse. A substantial amount of research has been conducted in the past to detect clones. A majority of these approaches use lexical and syntactic information to detect clones. However, only a few of them target semantic clones. Recently, motivated by the success of deep learning models in other fields, including natural language processing and computer vision, researchers have attempted to adopt deep learning techniques to detect code clones. These approaches use lexical information (tokens) and(or) syntactic structures like abstract syntax trees (ASTs) to detect code clones. However, they do not make sufficient use of the available structural and semantic information hence, limiting their capabilities. This paper addresses the problem of semantic code clone detection using program dependency graphs and geometric neural networks, leveraging the structured syntactic and semantic information. We have developed a prototype tool HOLMES, based on our novel approach and empirically evaluated it on popular code clone benchmarks. Our results show that HOLMES performs considerably better than the other state-of-the-art tool, TBCCD. We also evaluated HOLMES on unseen projects and performed cross dataset experiments to assess the generalizability of HOLMES. Our results affirm that HOLMES outperforms TBCCD since most of the pairs that HOLMES detected were either undetected or suboptimally reported by TBCCD. |
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MEHROTRA, Nikita AGARWAL, Navdha GUPTA, Piyush ANAND, Saket LO, David PURANDARE, Rahul |
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MEHROTRA, Nikita AGARWAL, Navdha GUPTA, Piyush ANAND, Saket LO, David PURANDARE, Rahul |
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MEHROTRA, Nikita |
title |
Modeling functional similarity in source code with graph-based Siamese networks |
title_short |
Modeling functional similarity in source code with graph-based Siamese networks |
title_full |
Modeling functional similarity in source code with graph-based Siamese networks |
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Modeling functional similarity in source code with graph-based Siamese networks |
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Modeling functional similarity in source code with graph-based Siamese networks |
title_sort |
modeling functional similarity in source code with graph-based siamese networks |
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Institutional Knowledge at Singapore Management University |
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2022 |
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https://ink.library.smu.edu.sg/sis_research/7658 https://ink.library.smu.edu.sg/context/sis_research/article/8661/viewcontent/2011.11228.pdf |
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