Adding context to source code representations for deep learning
Deep learning models have been successfully applied to a variety of software engineering tasks, such as code classification, summarisation, and bug and vulnerability detection. In order to apply deep learning to these tasks, source code needs to be represented in a format that is suitable for input...
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sg-smu-ink.sis_research-98272024-06-06T09:37:17Z Adding context to source code representations for deep learning TIAN, Fuwei TREUDE, Christoph Deep learning models have been successfully applied to a variety of software engineering tasks, such as code classification, summarisation, and bug and vulnerability detection. In order to apply deep learning to these tasks, source code needs to be represented in a format that is suitable for input into the deep learning model. Most approaches to representing source code, such as tokens, abstract syntax trees (ASTs), data flow graphs (DFGs), and control flow graphs (CFGs) only focus on the code itself and do not take into account additional context that could be useful for deep learning models. In this paper, we argue that it is beneficial for deep learning models to have access to additional contextual information about the code being analysed. We present preliminary evidence that encoding context from the call hierarchy along with information from the code itself can improve the performance of a state-of-the-art deep learning model for two software engineering tasks. We outline our research agenda for adding further contextual information to source code representations for deep learning. 2022-10-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/8824 info:doi/10.1109/ICSME55016.2022.00042 https://ink.library.smu.edu.sg/context/sis_research/article/9827/viewcontent/795600a374.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 additional context deep learning Source code representation Software Engineering |
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additional context deep learning Source code representation Software Engineering TIAN, Fuwei TREUDE, Christoph Adding context to source code representations for deep learning |
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Deep learning models have been successfully applied to a variety of software engineering tasks, such as code classification, summarisation, and bug and vulnerability detection. In order to apply deep learning to these tasks, source code needs to be represented in a format that is suitable for input into the deep learning model. Most approaches to representing source code, such as tokens, abstract syntax trees (ASTs), data flow graphs (DFGs), and control flow graphs (CFGs) only focus on the code itself and do not take into account additional context that could be useful for deep learning models. In this paper, we argue that it is beneficial for deep learning models to have access to additional contextual information about the code being analysed. We present preliminary evidence that encoding context from the call hierarchy along with information from the code itself can improve the performance of a state-of-the-art deep learning model for two software engineering tasks. We outline our research agenda for adding further contextual information to source code representations for deep learning. |
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text |
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TIAN, Fuwei TREUDE, Christoph |
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TIAN, Fuwei TREUDE, Christoph |
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TIAN, Fuwei |
title |
Adding context to source code representations for deep learning |
title_short |
Adding context to source code representations for deep learning |
title_full |
Adding context to source code representations for deep learning |
title_fullStr |
Adding context to source code representations for deep learning |
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Adding context to source code representations for deep learning |
title_sort |
adding context to source code representations for deep learning |
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Institutional Knowledge at Singapore Management University |
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2022 |
url |
https://ink.library.smu.edu.sg/sis_research/8824 https://ink.library.smu.edu.sg/context/sis_research/article/9827/viewcontent/795600a374.pdf |
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