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A systematic mappin...
A systematic mapping study of source code representation for deep learning in software engineering
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- Samoaa, Hazem Peter (författare)
- Gothenburg University,Göteborgs universitet,Institutionen för data- och informationsteknik, datavetenskap, programmeringslogik (GU),Department of Computer Science and Engineering, Computing Science, Programming Logic,Chalmers tekniska högskola,Chalmers University of Technology
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- Bayram, Firas (författare)
- Karlstads universitet,Institutionen för matematik och datavetenskap (from 2013)
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- Salza, P. (författare)
- Universität Zürich,University of Zürich,University of Zurich, CHE
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- Leitner, Philipp, 1982 (författare)
- Gothenburg University,Göteborgs universitet,Institutionen för data- och informationsteknik (GU),Department of Computer Science and Engineering (GU),Chalmers tekniska högskola,Chalmers University of Technology
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(creator_code:org_t)
- 2022-06-07
- 2022
- Engelska.
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Ingår i: Iet Software. - : Institution of Engineering and Technology (IET). - 1751-8806 .- 1751-8814. ; 16:4, s. 351-385
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https://research.cha... (primary) (free)
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https://doi.org/10.1...
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https://gup.ub.gu.se...
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https://doi.org/10.1...
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https://urn.kb.se/re...
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Abstract
Ämnesord
Stäng
- The usage of deep learning (DL) approaches for software engineering has attracted much attention, particularly in source code modelling and analysis. However, in order to use DL, source code needs to be formatted to fit the expected input form of DL models. This problem is known as source code representation. Source code can be represented via different approaches, most importantly, the tree-based, token-based, and graph-based approaches. We use a systematic mapping study to investigate i detail the representation approaches adopted in 103 studies that use DL in the context of software engineering. Thus, studies are collected from 2014 to 2021 from 14 different journals and 27 conferences. We show that each way of representing source code can provide a different, yet orthogonal view of the same source code. Thus, different software engineering tasks might require different (combinations of) code representation approaches, depending on the nature and complexity of the task. Particularly, we show that it is crucial to define whether the DL approach requires lexical, syntactical, or semantic code information. Our analysis shows that a wide range of different representations and combinations of representations (hybrid representations) are used to solve a wide range of common software engineering problems. However, we also observe that current research does not generally attempt to transfer existing representations or models to other studies even though there are other contexts in which these representations and models may also be useful. We believe that there is potential for more reuse and the application of transfer learning when applying DL to software engineering tasks.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Språkteknologi (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Language Technology (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Programvaruteknik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Software Engineering (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- performance
- network
- Computer Science
- Computer Science
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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