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This study examines what kinds of comments novice students document in source code, then categorizes those comments using a machine learning workflow. Manual classification is followed by an automated model that predicts comment types and enables generated feedback. Results show dominant categories including Literal and Insufficient, with additional taxonomy extensions and classifier evaluation.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/investigating-novice-developers-code-commenting-trends-using-machine-learning-techniques-290142/290142/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/investigating-novice-developers-code-commenting-trends-using-machine-learning-techniques-290142/290142.png","ImageObject",300,407,{"name":92,"@type":93},"Jiven","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-17","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the main objective of the study on novice developers' code commenting trends?","Question",{"text":112,"@type":113},"To identify the types of code comments novice students document and categorize them using machine learning to enable automated feedback.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How are code comments categorized in the research?",{"text":117,"@type":113},"The approach uses initial manual classification, then builds a machine learning model to automatically classify student source code comments into defined categories.",{"name":119,"@type":110,"acceptedAnswer":120},"Which machine learning classifier showed the highest overall accuracy, according to the findings?",{"text":121,"@type":113},"The Decision Tree classifier achieved the highest overall accuracy, reported as 85%.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},290142,1789640788,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":39,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":46,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":129,"read_time":31,"preview_page_count":4},1099513958607,"https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399","Article  \nInvestigating Novice Developers’ Code Commenting Trends Using Machine Learning Techniques  \nNiazi, Tahira, Das, Teerath, Ahmed, Ghufran, Waqas, Syed Muhammad, Khan, Sumra, Khan, Suleman, Abdelatif, Ahmed Abdelaziz and Wasi, Shaukat  \nAvailable at [https://clok. uclan.ac. uk/45341/](https://clok. uclan.ac. uk/45341/)  \nNiazi, Tahira, Das, Teerath, Ahmed, Ghufran, Waqas, Syed Muhammad, Khan, Sumra, Khan, Suleman, Abdelatif, Ahmed Abdelaziz and Wasi, Shaukat (2023) Investigating Novice Developers’ Code Commenting Trends Using Machine Learning Techniques. Algorithms, 16 (1). p. 53.  \nIt is advisable to refer to the publisher’s version if you intend to cite from the work. [http://dx.doi.org/10.3390/a16010053](http://dx.doi.org/10.3390/a16010053)  \nFor more information about UCLan’s research in this area go to  \n[http://www.uclan.ac.uk/researchgroups/](http://www.uclan.ac.uk/researchgroups/ and)[ and](http://www.uclan.ac.uk/researchgroups/ and) search for \u003Cname of research Group>.  \nFor information about Research generally at UCLan please go to  \n[http://www.uclan.ac.uk/research/](http://www.uclan.ac.uk/research/)  \nAll outputs in CLoK are protected by Intellectual Property Rights law, including Copyright law. Copyright, IPR and Moral Rights for the works on this site are retained by the individual authors and/or other copyright owners. Terms and conditions for use of this material are defined in the policies page.  \nCLoK  \nCentral Lancashire online Knowledge [www.clok. uclan.ac. uk](www.clok. uclan.ac. uk)  \nalgorithms  \nArticle  \nInvestigating Novice Developers' Code Commenting Trends Using Machine Learning Techniques  \nTahira Niazi 1, Teerath Das 2, Ghufran Ahmed 3, Syed Muhammad Waqas 4, Sumra Khan 1, Suleman Khan 5,*, Ahmed Abdelaziz Abdelatif 6 and Shaukat Wasi 1  \nCitation: Niazi, T.; Das, T.;  \nAhmed, G.; Waqas, S.M.; Khan, S.; Khan, S.; Abdelatif, A.A.; Wasi, S. Investigating Novice Developers'Code Commenting Trends Using Machine Learning Techniques. Algorithms 2023, 16, 53. [https://](https://)[ ](https://)[doi.org/10.3390/a16010053](doi.org/10.3390/a16010053)  \nAcademic Editors: Xiang Zhang and Xiaoxiao Li  \nReceived: 12 October 2022  \nRevised: 2 January 2023  \nAccepted: 4 January 2023  \nPublished: 12 January 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Computer Science, Mohammad Ali Jinnah University, Karachi 75400, Pakistan  \n2 Faculty of Information Technology, University of Jyväskylä, 40014 Jyväskylä, Finland  \n3 School of Computing, National University of Computer Emerging Sciences, Karachi 75400, Pakistan  \n4 Department of Computer Science, Bahria University, Karachi 75260, Pakistan  \n5 School of Psychology and Computer Science, University of Central Lancashire, Preston PR1 2HE, UK  \n6 Khawarizmi International College, Al Bahya, Abu Dhabi 25669, United Arab Emirates  \n* Correspondence: [skhan92@uclan.ac.uk](skhan92@uclan.ac.uk)  \nAbstract: Code comments are considered an efficient way to document the functionality of a particular block of code. Code commenting is a common practice among developers to explain the purpose of the code in order to improve code comprehension and readability. Researchers investigated the effect of code comments on software development tasks and demonstrated the use of comments in several ways, including maintenance, reusability, bug detection, etc. Given the importance of code comments, it becomes vital for novice developers to brush up on their code commenting skills. In this study, we initially investigated what types of comments novice students document in their source code and further categorized those comments using a machine learning approach","cbCaibRMxHSRLtPn","https://ap.wps.com/l/cbCaibRMxHSRLtPn","pdf",3053046,"English","# Abstract\n# Keywords\n# Introduction","[{\"question\":\"What is the main objective of the study on novice developers' code commenting trends?\",\"answer\":\"To identify the types of code comments novice students document and categorize them using machine learning to enable automated feedback.\"},{\"question\":\"How are code comments categorized in the research?\",\"answer\":\"The approach uses initial manual classification, then builds a machine learning model to automatically classify student source code comments into defined categories.\"},{\"question\":\"Which machine learning classifier showed the highest overall accuracy, according to the findings?\",\"answer\":\"The Decision Tree classifier achieved the highest overall accuracy, reported as 85%.\"}]","Investigating Novice Developers' Code Commenting Trends Using Machine Learning Techniques | PDF"]