[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117201-en":3,"doc-seo-117201-105":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},117201,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","A Survey on Machine Learning Techniques Applied to Source Code","Advances in machine learning have driven rapid adoption of ML methods for software engineering tasks that rely on source code analysis, including testing and vulnerability detection. The growing volume of related studies makes it difficult for the community to understand the current research landscape. This paper summarizes knowledge on applied machine learning for source code analysis by reviewing 494 studies across twelve software engineering task categories, along with their techniques, tools, and datasets, and highlights increasing adoption plus key challenges.","Journal Pre-proof  \nA survey on machine learning techniques applied to source code  \nTushar Sharma, Maria Kechagia, Stefanos Georgiou, Rohit Tiwari, Indira Vats, Hadi Moazen, Federica Sarro  \nPII: S0164-1212(23)00329-1  \nDOI: [https://doi.org/10.1016/j.jss.2023.111934](https://doi.org/10.1016/j.jss.2023.111934)  \nReference: JSS 111934  \nTo appear in: The Journal of Systems & Software  \nReceived date : 16 April 2023  \nRevised date : 10 November 2023  \nAccepted date : 14 December 2023  \nPlease cite this article as: T. Sharma, M. Kechagia, S. Georgiou et al., A survey on machine learning techniques applied to source code. The Journal of Systems & Software (2023), doi:  \n[https://doi.org/10.1016/j.jss.2023.111934](https://doi.org/10.1016/j.jss.2023.111934) .  \nThis is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.  \n© 2023 Published by Elsevier Inc.  \nJournal Pre-proof  \n􀀀 For correspondence:  \n[tushar@dal.ca](tushar@dal.ca)  \nData availability: Replication package can be found on GitHub-[https://github.com/-](https://github.com/-)tushartushar/ML4SCA  \nFunding: Maria Kechagia and Federica Sarro are supported by the ERC grant no. 741278 (EPIC) .  \n1 A Survey on Machine Learning  \n2 Techniques Applied to Source Code  \n3 Tushar Sharma1 􀀀, Maria Kechagia2, Stefanos Georgiou3, Rohit Tiwari4, Indira  \n4 Vats5, Hadi Moazen6, Federica Sarro2  \n5 1 Dalhousie University, Canada; 2 University College London, United Kingdom; 3 Queens  \n6 University, Canada; 4 DevOn, India; 5J.S.S. Academy of Technical Education, India; 6 Sharif  \n7 University of Technology, Iran  \n8 ~~ ~~  \n9 Abstract  \n10 The advancements in machine learning techniques have encouraged researchers to apply these  \n11 techniques to a myriad of software engineering tasks that use source code analysis, such as  \n12 testing and vulnerability detection. Such a large number of studies hinders the community from  \n13 understanding the current research landscape. This paper aims to summarize the current  \n14 knowledge in applied machine learning for source code analysis. We review studies belonging to  \n15 twelve categories of software engineering tasks and corresponding machine learning techniques, 16 tools, and datasets that have been applied to solve them. To do so, we conducted an extensive  \n17 literature search and identified 494 studies. We summarize our observations and findings with  \n18 the help of the identified studies. Our findings suggest that the use of machine learning  \n19 techniques for source code analysis tasks is consistently increasing. We synthesize commonly  \n20 used steps and the overall workflow for each task and summarize machine learning techniques  \n21 employed. We identify a comprehensive list of available datasets and tools useable in this  \n22 context. Finally, the paper discusses perceived challenges in this area, including the availability of  \n23 standard datasets, reproducibility and replicability, and hardware resources.  \n24 ~~ ~~  \n25 Keywords: Machine learning for software engineering, source code analysis, deep learning, datasets, 26 tools.  \n27 1. Introduction  \n28 In the last two decades, we have witnessed significant advancements in Machine Learning (ml), 29 including Deep Learning (dl) techniques, specifically in the domain of image [237, 476], text [255, 4], 30 and speech [418, 166, 165] processing. These advancements, coupled with a large amount of  \n31 open-source code and associated artifacts, as well as the availability of a","cbCaiutjEARVwveC","https://ap.wps.com/l/cbCaiutjEARVwveC","pdf",2128813,1,104,"English","en",105,"# Abstract\n# Keywords\n# Introduction\n## Motivation and scope\n## Role of literature surveys","[{\"question\":\"What is the main purpose of the survey?\",\"answer\":\"The survey aims to summarize current knowledge on applied machine learning techniques for source code analysis by organizing studies across software engineering task categories.\"},{\"question\":\"How were studies selected and what did the authors find?\",\"answer\":\"An extensive literature search identified 494 studies. The survey reports that the use of ML techniques for source code analysis tasks is consistently increasing.\"},{\"question\":\"Which topics and challenges does the paper discuss for this research area?\",\"answer\":\"The paper synthesizes common workflow steps, compiles available datasets and tools, and discusses challenges including the availability of standard datasets, reproducibility and replicability, and hardware resources.\"}]","A Survey on Machine Learning Techniques Applied to Source Code | PDF",1785674394,262,{"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-survey-on-machine-learning-techniques-applied-to-source-code","",{"@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-survey-on-machine-learning-techniques-applied-to-source-code/117201/",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 main purpose of the survey?","Question",{"text":75,"@type":76},"The survey aims to summarize current knowledge on applied machine learning techniques for source code analysis by organizing studies across software engineering task categories.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were studies selected and what did the authors find?",{"text":80,"@type":76},"An extensive literature search identified 494 studies. The survey reports that the use of ML techniques for source code analysis tasks is consistently increasing.",{"name":82,"@type":73,"acceptedAnswer":83},"Which topics and challenges does the paper discuss for this research area?",{"text":84,"@type":76},"The paper synthesizes common workflow steps, compiles available datasets and tools, and discusses challenges including the availability of standard datasets, reproducibility and replicability, and hardware resources.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]