[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119251-en":3,"doc-seo-119251-105":30,"detail-sidebar-cat-0-en-105":92},{"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":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},119251,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","A study on Fault Identification in Continuous Integration Pipelines using Machine Learning","The thesis investigates fault identification and classification for failed test cases within Continuous Integration (CI) pipelines in Ericsson’s RUX radio software laboratory. Verification engineers debug CI failures that may stem from hardware (test environment or radio hardware) or software (framework or radio software), often requiring manual search of historical results and iterative reruns with different versions. The work develops a machine-learning model trained on automated reruns and CI test outcomes from multiple radio types to classify failure cause and support clustering/correlation for deeper diagnostic insights, aiming to reduce lead time and resource consumption.","A study on Fault Identification in Continuous Integration Pipelines using Machine Learning  \nBachelor of Science Thesis in Software Engineering and Management  \nZubeen S Maruf  \nDepartment of Computer Science and Engineering UNIVERSITY OF GOTHENBURG  \nCHALMERS UNIVERSITY OF TECHNOLOGY Gothenburg, Sweden 2023  \nThe Author grants to University of Gothenburg and Chalmers University of Technology the non-exclusive right to publish the Work electronically and in a noncommercial purpose make it accessible on the Internet.  \nThe Author warrants that he/she is the author to the Work, and warrants that the Work does not contain text, pictures or other material that violates copyright law.  \nThe Author shall, when transferring the rights of the Work to a third party (for example a publisher or a company), acknowledge the third party about this agreement. If the Author has signed a copyright agreement with a third party regarding the Work, the Author warrants hereby that he/she has obtained any necessary permission from this third party to let University of Gothenburg and Chalmers University of Technology store the Work electronically and make it accessible on the Internet.  \nA study on Fault Identification in Continuous Integration Pipelines using Machine Learning  \nIn collaboration with Ericsson  \n© Zubeen S Maruf, June, 2023 .  \nSupervisor: Gregory Gay  \nExaminer: Daniel Str¨uber  \nUniversity of Gothenburg  \nChalmers University of Technology  \nDepartment of Computer Science and Engineering SE-412 96 G¨oteborg  \nSweden  \nTelephone + 46 (0)31-772 1000  \nDepartment of Computer Science and Engineering University of Gothenburg  \nChalmers University of Technology Gothenburg, Sweden 2023  \nA study on Fault Identification in Continuous Integration Pipelines using Machine Learning  \n1st Zubeen S Maruf  \nUniversity of Gothenburg Gothenburg, Sweden  \n[gusmarzu@student.gu.se](gusmarzu@student.gu.se)  \nI. INTRODUCTION  \nA. Background  \nEricsson is a telecommunication company working with development, network systems, and software development and running operations for telecom service providers. Teams at Ericsson provided end-to-end services for developing telecommunication products and services. Companies like Ericsson develop, deliver and manage products by providing hardware, software, and services to enable customer satisfaction and are expanding rapidly. For such growth and scalability, the demand for machine learning has risen [3] due to the technological shift and increasingly complex systems. The more complex the system, the more time developers spend debugging problems and determining if the problem was caused by hardware or software failure. Machine learning techniques could potentially assist in identifying software or hardware issues for more cost-effective and efficient fault detection and diagnosis methods in such complex systems. RUX is a lab at Ericsson that falls under the radio software group and is focused on radio performance and verification. The lab specializes in detecting and quickly visualizing changes in Radio performance on the module level through the life-cycle of Radio hardware and radio software. Verification engineers working with RUX debug failed test cases running in a Continuous Integration (CI) loop. These failed test cases result from hardware (the testing environment and the radio itself) or software (the framework and radio software) problems.  \nB. Problem Domain & Motivation  \nEricsson has a distributed working environment where endto-end testing ensures maximum output capacity for product releases. Verification engineers working from multiple countries in one team ensure a full cycle of end-to-end testing in the RUX lab [11] . There are many benefits of working end-to-end to provide maximum support to the project, but there are some challenges when work depends on teamwork among colleagues. Engineers sometimes require consultation from colleagues working in a different time zone to debug issues. To minimize th","cbCaihtpALvVlITS","https://ap.wps.com/l/cbCaihtpALvVlITS","pdf",717806,1,12,"English","en",105,"# Introduction\n## Background\n## Problem Domain & Motivation\n## Purpose","[{\"question\":\"What problem does the thesis address in CI pipelines at Ericsson’s RUX lab?\",\"answer\":\"It addresses how to classify why CI test cases fail, distinguishing whether failures originate from hardware components or software components in the testing and radio stacks.\"},{\"question\":\"How does the study plan to collect training data for the machine-learning model?\",\"answer\":\"It uses automated reruns of test cases with different radio software versions and collects CI test results across multiple radio types as the dataset for analysis.\"},{\"question\":\"What outcomes does the machine-learning approach aim to achieve for verification engineers?\",\"answer\":\"It aims to automate fault classification to speed troubleshooting, reduce manual effort in searching past results and rerunning tests, and provide insights through potential clustering or correlation of failure patterns.\"}]","A study on Fault Identification in Continuous Integration Pipelines using Machine Learning | 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problem does the thesis address in CI pipelines at Ericsson’s RUX lab?","Question",{"text":76,"@type":77},"It addresses how to classify why CI test cases fail, distinguishing whether failures originate from hardware components or software components in the testing and radio stacks.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the study plan to collect training data for the machine-learning model?",{"text":81,"@type":77},"It uses automated reruns of test cases with different radio software versions and collects CI test results across multiple radio types as the dataset for analysis.",{"name":83,"@type":74,"acceptedAnswer":84},"What outcomes does the machine-learning approach aim to achieve for verification engineers?",{"text":85,"@type":77},"It aims to automate fault classification to speed troubleshooting, reduce manual effort in searching past results and rerunning tests, and provide insights through potential clustering or correlation of failure 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