[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118639-en":3,"doc-seo-118639-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118639,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","A systematic mapping study on graph machine learning for static source code analysis","Graph machine learning, especially graph neural networks, has become widely used across domains including static source code analysis, where learning models exploit rich networks of relations and entities. A comprehensive, systematic overview of methods for this application had been missing. This study provides a broad state-of-the-art map of techniques, based on a systematic mapping of 4499 studies with 323 primary studies. It identifies seven sub-domains and catalogues artefacts, graph representations, features, and models, showing growth since 2018 and highlighting future work on under-explored areas and interpretability.","University of Groningen  \nA systematic mapping study on graph machine learning for static source code analysis  \nMaarleveld, Jesse; Guo, Jiapan; Feitosa, Daniel  \nPublished in:  \nInformation and Software Technology  \nDOI:  \n10.1016/j.infsof.2025.107722  \nIMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below.  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nPublication date: 2025  \nLink to publication in University of Groningen/UMCG research database  \nCitation for published version (APA):  \nMaarleveld, J. , Guo, J. , & Feitosa, D. (2025) . A systematic mapping study on graph machine learning for static source code analysis. Information and Software Technology , 183, Article 107722.  \n[https://doi.org/10.1016/j.infsof.2025.107722](https://doi.org/10.1016/j.infsof.2025.107722)  \nCopyright  \nOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons) .  \nThe publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license. More information can be found on the University of Groningen website: [https://www.rug.nl/library/open-access/self-archiving-pure/taverne](https://www.rug.nl/library/open-access/self-archiving-pure/taverne)amendment.  \nTake-down policy  \nIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.  \nDownloaded from the University of Groningen/U MCG research database (Pure): [http://www.rug. nl/research/portal. For technical reasons the](http://www.rug. nl/research/portal. For technical reasons the)[ ](http://www.rug. nl/research/portal. For technical reasons the)[number of authors shown on this cover page is limited to 10 maximum.](number of authors shown on this cover page is limited to 10 maximum.)  \nDownload date: 30-12-2025  \nInformation and Software Technology 183 (2025) 107722  \n| A systematic mapping study on graph machine learning for static source code analysis\u003Cbr>Jesse Maarleveld ∗, Jiapan Guo, Daniel Feitosa\u003Cbr>Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Nijenborgh 9, 9747 AG, Groningen, The Netherlands |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O\u003Cbr>Dataset link: [https://doi.org/10.5281/zenodo.1](https://doi.org/10.5281/zenodo.1)[ ](https://doi.org/10.5281/zenodo.1)4770542\u003Cbr>Keywords:\u003Cbr>Graph machine learning\u003Cbr>Graph neural networks Static source code analysis Systematic mapping study | A B S T R A C T\u003Cbr>Context: In recent years, graph machine learning and particularly graph neural networks have seen successful and widespread applications in many fields, including static source code analysis. Such machine learning techniques enable learning on rich information networks capable of representing different relations and entities.\u003Cbr>However, there have been no comprehensive studies investigating the use of graph machine learning for static source code analysis. There is no complete systematic picture of what techniques may be considered tried and tested, and where opportunities for future improvements can still be found.\u003Cbr>Objective: The main goal of this study is to provide a broad overview of the state of the art of static source code analysis using graph machine learning.\u003Cbr>Methods: A systematic mapping was performed covering 4499 studies, presenting a final selection of 323 primary studies.\u003Cbr>Results: Among the selected studies, seven major sub-domains were identified. The use and combinations of artefacts, different graph representations, different features, and different machine learning models used were collected and categorised.\u003Cb","cbCainX1oCmIQ0YF","https://ap.wps.com/l/cbCainX1oCmIQ0YF","pdf",1875358,1,18,"English","en",105,"# Introduction\n# Background\n## Graphs for graph machine learning\n## Traditional graph machine learning\n## Graph neural networks\n## Tree neural networks\n## Metapaths","[{\"question\":\"What is the objective of this systematic mapping study?\",\"answer\":\"The study aims to provide a broad overview of the state of the art of static source code analysis using graph machine learning.\"},{\"question\":\"How was the evidence collected and selected?\",\"answer\":\"A systematic mapping covered 4499 studies, resulting in a final selection of 323 primary studies.\"},{\"question\":\"What trends and future opportunities does the study report?\",\"answer\":\"Graph learning, particularly graph neural networks, increased significantly since 2018. Future opportunities include deeper exploration of under-explored domains, adding more artefacts beyond source code, and improving interpretability and explainability.\"}]","A systematic mapping study on graph machine learning for static source code analysis | PDF",1785684642,45,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"a-systematic-mapping-study-on-graph-machine-learning-for-static-source-code-analysis","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/a-systematic-mapping-study-on-graph-machine-learning-for-static-source-code-analysis/118639/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the objective of this systematic mapping study?","Question",{"text":75,"@type":76},"The study aims to provide a broad overview of the state of the art of static source code analysis using graph machine learning.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the evidence collected and selected?",{"text":80,"@type":76},"A systematic mapping covered 4499 studies, resulting in a final selection of 323 primary studies.",{"name":82,"@type":73,"acceptedAnswer":83},"What trends and future opportunities does the study report?",{"text":84,"@type":76},"Graph learning, particularly graph neural networks, increased significantly since 2018. 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