[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124683-en":3,"doc-seo-124683-105":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},124683,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","An Architectural Technical Debt Index Based on Machine Learning and Architectural Smells - A novel approach to estimate architectural technical debt principal","Technical debt (TD) management depends on measuring the principal effort already accumulated in software systems, yet architectural TD estimation remains insufficiently automated, freely available, and thoroughly validated. The study proposes an approach that predicts the severity of an architectural smell using a learning-to-rank machine learning model and statically counts the exact lines of code responsible for each smell. A case study and 16 practitioner interviews evaluate representativeness, with 71% agreement on refactoring effort.","University of Groningen  \nAn Architectural Technical Debt Index Based on Machine Learning and Architectural Smells  \nSas, Darius; Avgeriou, Paris  \nPublished in:  \nIEEE Transactions on Software Engineering  \nDOI:  \n10.1109/TSE.2023.3286179  \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: 2023  \nLink to publication in University of Groningen/UMCG research database  \nCitation for published version (APA):  \nSas, D. , & Avgeriou, P. (2023) . An Architectural Technical Debt Index Based on Machine Learning and Architectural Smells. IEEE Transactions on Software Engineering , 49(8), 4169-4195.  \n[https://doi.org/10.1109/TSE.2023.3286179](https://doi.org/10.1109/TSE.2023.3286179)  \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: 04-08-2026  \nAn Architectural Technical Debt Index Based on Machine Learning and Architectural Smells  \nDarius Sas  and Paris Avgeriou   \nAbstract—A key aspect of technical debt (TD) management is the ability to measure the amount of principal accumulated in a system. The current literature contains an array of approaches to estimate TD principal, however, only a few ofthem focus specificallyon architectural TD, but none of them satisfies all three of the following criteria: being fully automated, freely available, and thoroughly validated. Moreover, a recent study has shown that many of the current approaches suffer from certain shortcomings, such as relying on hand-picked thresholds. In this article, we propose a novel approach to estimate architectural technical debt principal based on machine learning and architectural smells to address such shortcomings. Our approach can estimate the amount of technical debt principal generated by a single architectural smell instance. To do so, we adopt novel techniques from Information Retrieval to train a learning-to-rank machine learning model(more specifically, a gradient boosting machine) that estimates the severity of an architectural smell and ensure the transparency of the predictions. Then, for each instance, we statically analyse the source code to calculate the exact number of lines of code creating the smell. Finally, we combine these two values to calculate the technical debt principal. To validate the approach, we conducted a case study and interviewed 16 practitioners, from both open source and industry, and asked them about their opinions on the TD principal estimations for several smells detected in their projects. The results show that for 71% of instances, practitioners agreed that the estimations provided were representative of the effort necessary to refactor the smell.  \nIndex Terms","cbCairiMdw2PoYy6","https://ap.wps.com/l/cbCairiMdw2PoYy6","pdf",4460332,1,28,"English","en",105,"# Introduction\n## Technical debt principal and interest\n# Proposed approach\n## Learning-to-rank severity estimation\n## Static code analysis for smell-related lines of code\n## Combining severity and code evidence to compute principal\n# Validation and study design\n## Case study and practitioner interviews","[{\"question\":\"What problem does the paper address in technical debt management?\",\"answer\":\"It targets measuring the principal effort of architectural technical debt, which prior approaches inadequately estimate under requirements such as full automation, free availability, and thorough validation.\"},{\"question\":\"How does the approach estimate architectural technical debt principal?\",\"answer\":\"It predicts smell severity using a learning-to-rank machine learning model, then performs static source-code analysis to count lines of code creating the smell, combining both signals into the principal estimate.\"},{\"question\":\"How was the proposed method validated and what were the results?\",\"answer\":\"Validation used a case study with 16 practitioners from open source and industry, assessing estimates for smells in their projects; for 71% of instances, practitioners agreed the estimates reflected refactoring effort.\"}]","An Architectural Technical Debt Index Based on Machine Learning and Architectural Smells - A novel approach to estimate architectural technical debt principal | PDF",1785893907,71,{"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},"an-architectural-technical-debt-index-based-on-machine-learning-and-architectural-smells-a-novel-approach-to-estimate-architectural-technical-debt-principal","",{"@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/an-architectural-technical-debt-index-based-on-machine-learning-and-architectural-smells-a-novel-approach-to-estimate-architectural-technical-debt-principal/124683/",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","2026-08-05",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 problem does the paper address in technical debt management?","Question",{"text":75,"@type":76},"It targets measuring the principal effort of architectural technical debt, which prior approaches inadequately estimate under requirements such as full automation, free availability, and thorough validation.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the approach estimate architectural technical debt principal?",{"text":80,"@type":76},"It predicts smell severity using a learning-to-rank machine learning model, then performs static source-code analysis to count lines of code creating the smell, combining both signals into the principal estimate.",{"name":82,"@type":73,"acceptedAnswer":83},"How was the proposed method validated and what were the results?",{"text":84,"@type":76},"Validation used a case study with 16 practitioners from open source and industry, assessing estimates for smells in their projects; 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