[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126224-en":3,"doc-seo-126224-105":31,"detail-sidebar-cat-0-en-105":93},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126224,549768702563,"Sage","https://ap-avatar.wpscdn.com/avatar/8000c4aa63b76e948b?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786536092046926083",8,"Research & Report","Applicability of Supervised Machine Learning for CI Configuration Selection - A Case Study in the Telecom Industry","This study introduces a supervised machine learning model that assigns CI configurations to test specifications with higher accuracy than existing approaches. Current optimization methods cannot handle per-test-case selection and mapping into predefined CI configuration groups. The solution uses an ensemble architecture with three sub-models plus a rule-based component, and analyzes the features driving assignments. A decision support system evaluates applicability via a domain-expert survey, showing performance beyond expert requirements. Results also indicate business impact through reduced misassignments, saved time, and lower fault slip, with future work toward automation and more complex ML models.","Applicability of Supervised Machine Learning for CI Configuration Selection  \nA Case Study in the Telecom Industry  \nMaster’s thesis in Computer science and engineering  \nALBIN LÖNNFÄLT & VIKTOR TU  \nDepartment of Computer Science and Engineering CHALMERS UNIVERSITY OF TECHNOLOGY UNIVERSITY OF GOTHENBURG  \nGothenburg, Sweden 2023  \nMaster’s thesis 2023  \nApplicability of Supervised Machine Learning for CI Configuration Selection  \nA Case Study in the Telecom Industry  \nALBIN LÖNNFÄLT & VIKTOR TU  \nDepartment of Computer Science and Engineering Chalmers University of Technology University of Gothenburg Gothenburg, Sweden 2023  \nApplicability of Supervised Machine Learning for CI Configuration Selection A Case Study in the Telecom Industry  \nALBIN LÖNNFÄLT & VIKTOR TU  \n© ALBIN LÖNNFÄLT & VIKTOR TU, 2023 .  \nSupervisor: Gregory Gay, Department of Computer Science and Engineering  \nAdvisor: Sahar Tahvili, Ph.D., Ericsson AB  \nExaminer: Lucas Gren, Department of Computer Science and Engineering  \nMaster’s Thesis 2023  \nDepartment of Computer Science and Engineering  \nChalmers University of Technology and University of Gothenburg SE-412 96 Gothenburg  \nTelephone +46 31 772 1000  \nTypeset in LATEX  \nGothenburg, Sweden 2023  \nApplicability of Supervised Machine Learning for CI Configuration Selection A Case Study in the Telecom Industry  \nAlbin Lönnfält & Viktor Tu  \nDepartment of Computer Science and Engineering  \nChalmers University of Technology and University of Gothenburg  \nAbstract  \nThis study introduces a novel supervised machine learning (ML) model for accurately assigning CI configurations to test specifications. Current solutions to optimize selection of CI configurations lack the ability to select CI configurations for individual test cases and assigning them into predefined CI configurations. The model employs an ensemble architecture with three sub-models and a rule-based component, each focusing on specific aspects of the problem. Extensive model analysis reveals important features that contribute to the assignment process. A decision support system based on the ML model is developed to evaluate the applicability of supervised ML in CI configuration assignment, validated through a survey study involving domain experts. The study demonstrates that supervised ML can exceed the performance requirements of domain experts. Certain features in test specifications are found to be influential in the assignment outcome. Implementing supervised ML brings business value, reducing misassignments, saving time, and reducing fault slip through. Proposed future research includes exploring fully automated CI configuration assignments and investigating more complex ML models, such as neural networks, for enhanced performance and exploring the potential for fully automated adaptation.  \nKeywords: Software testing, continuous integration, continuous integration configuration, and supervised machine learning.  \nAcknowledgements  \nWe express our gratitude to Gregory Gay, our academic supervisor, for his guidance and feedback during this thesis. We also thank Sahar Tahvili, our industrial supervisor, and all the Ericsson employees involved. Lastly, we thank our families and friends for their support throughout this journey.  \nAlbin Lönnfält & Viktor Tu, Gothenburg, June 2023","cbCaigowB8BkYXG6","https://ap.wps.com/l/cbCaigowB8BkYXG6","pdf",4478509,7,1,99,"English","en",105,"# Abstract\n# Keywords\n# Acknowledgements","[{\"question\":\"What problem does the study address in CI configuration selection?\",\"answer\":\"It targets the challenge of assigning CI configurations to individual test specifications when current optimization solutions cannot select configurations per test case and map them into predefined CI configuration groups.\"},{\"question\":\"How is the proposed supervised ML model structured?\",\"answer\":\"The model uses an ensemble architecture with three sub-models and a rule-based component, where each part focuses on specific aspects of the assignment problem.\"},{\"question\":\"How is the model evaluated and what results are reported?\",\"answer\":\"A decision support system is developed and validated through a survey study with domain experts. The findings show supervised ML can exceed expert performance requirements and that specific features in test specifications strongly influence outcomes.\"}]","Applicability of Supervised Machine Learning for CI Configuration Selection - A Case Study in the Telecom Industry | PDF",1785903908,249,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"applicability-of-supervised-machine-learning-for-ci-configuration-selection-a-case-study-in-the-telecom-industry","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/applicability-of-supervised-machine-learning-for-ci-configuration-selection-a-case-study-in-the-telecom-industry/126224/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"What problem does the study address in CI configuration selection?","Question",{"text":77,"@type":78},"It targets the challenge of assigning CI configurations to individual test specifications when current optimization solutions cannot select configurations per test case and map them into predefined CI configuration groups.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How is the proposed supervised ML model structured?",{"text":82,"@type":78},"The model uses an ensemble architecture with three sub-models and a rule-based component, where each part focuses on specific aspects of the assignment problem.",{"name":84,"@type":75,"acceptedAnswer":85},"How is the model evaluated and what results are reported?",{"text":86,"@type":78},"A decision support system is developed and validated through a survey study with domain experts. 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