[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120442-en":3,"doc-seo-120442-105":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},120442,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Improving the Performance of Machine Learning-based Methods for Continuous Integration by Handling Noise - PhD Thesis","Modern software development increasingly adopts continuous integration (CI) to deliver high-quality features under market pressure. This thesis investigates how machine learning can optimize CI tasks, while addressing a key limitation: predictive performance is reduced by inaccurate and irrelevant information, i.e., noise, in data extracted from source code. Using design science research and controlled experiments, the work studies class and attribute noise-handling, builds regression-testing-oriented ML methods, and evaluates the impact on test case selection, build outcomes, and code change request predictions.","Thesis for The Degree of Doctor of Philosophy  \nImproving the Performance of Machine Learning-based Methods for Continuous Integration by Handling Noise  \nKhaled Walid Al-Sabbagh  \nDepartment of Computer Science and Engineering Chalmers University of Technology | University of Gothenburg  \nGothenburg, Sweden, 2023  \nImproving the Performance of Machine Learning-based Methods for Continuous Integration by Handling Noise  \nKhaled Walid Al-Sabbagh  \n© Khaled Walid Al-Sabbagh, 2023 except where otherwise stated.  \nAll rights reserved.  \nISBN: 978-91-8069-361-5 (PRINT)  \nISBN: 978-91-8069-362-2 (PDF)  \nDepartment of Computer Science and Engineering  \nDivision of Interaction Design and Software Engineering  \nChalmers University of Technology | University of Gothenburg SE-412 96 G¨oteborg,  \nSweden  \nPhone: +46(0)729250522  \nPrinted by Chalmers Digitaltryck, Gothenburg, Sweden 2023 .  \n“We are surrounded by data, but starved for insights.”  \n- Jay Baer  \nAbstract  \nBackground: Modern software development companies are increasingly implementing continuous integration (CI) practices to meet market demands for delivering high-quality features. The availability of data from CI systems presents an opportunity for these companies to leverage machine learning to create methods for optimizing the CI process.  \nProblem: The predictive performance of these methods can be hindered by inaccurate and irrelevant information – noise.  \nObjective: The goal of this thesis is to improve the effectiveness of machine learning-based methods for CI by handling noise in data extracted from source code.  \nMethods: This thesis employs design science research and controlled experiments to study the impact of noise-handling techniques in the context of CI. It involves developing ML-based methods for optimizing regression testing (MeBoTS and HiTTs), creating a taxonomy to reduce class noise, and implementing a class noise-handling technique (DB) . Controlled experiments are carried out to examine the impact of class noise-handling on MeBoTS’performance for CI.  \nResults: The thesis findings show that handling class noise using the DB technique improves the performance of MeBoTS in test case selection and code change request predictions. The F1-score increases from 25% to 84% in test case selection and the Recall improved from 15% to 25% in code change request prediction after applying DB. However, handling attribute noise through a removal-based technique does not impact MeBoTS’ performance, as the F1-score remains at 66% . For memory management and complexity code changes should be tested with performance, load, soak, stress, volume, and capacity tests. Additionally, using the “majority filter” algorithm improves MCC from 0.13 to 0.58 in build outcome prediction and from-0.03 to 0.57 in code change request prediction.  \nConclusions: In conclusion, this thesis highlights the effectiveness of applying different class noise handling techniques to improve test case selection, build outcomes, and code change request predictions. Utilizing small code commits for training MeBoTS proves beneficial in filtering out test cases that do not reveal faults. Additionally, the taxonomy of dependencies offers an efficient and effective way for performing regression testing. Notably, handling attribute noise does not improve the predictions of test execution outcomes.  \nKeywords  \nContinuous Integration, Machine Learning, Class Noise, Attribute Noise.  \nList of Publications  \nAppended publications  \nThis thesis is based on the following publications:  \n1. Al Sabbagh, K., Staron, M., Hebig, R., & Meding, W.(2019) . Predicting Test Case Verdicts Using Textual Analysis of Committed Code Churns. In IWSM-Mensura. 2019, pp. 138–153  \n2. Al-Sabbagh, K. W., Hebig, R., & Staron, M.(2020) . The effect of class noise on continuous test case selection: A controlled experiment on industrial data. In Product-Focused Software Process Improvement: 21st International Conference, PROFES 2020, Pr","cbCaigRzzVEFKoMM","https://ap.wps.com/l/cbCaigRzzVEFKoMM","pdf",15306818,1,252,"English","en",105,"# Abstract\n## Background and Problem\n## Objective\n## Methods and Evaluation\n## Results and Conclusions","[{\"question\":\"What problem does the thesis address in CI optimization using machine learning?\",\"answer\":\"It addresses that predictive performance of CI-related ML methods can be hindered by inaccurate and irrelevant information, referred to as noise, in data extracted from source code.\"},{\"question\":\"How does the thesis improve machine learning performance in continuous integration?\",\"answer\":\"It applies noise-handling techniques, including class noise handling, and develops ML-based methods such as those for regression testing, along with creating a taxonomy to reduce class noise.\"},{\"question\":\"What are the main findings regarding class noise versus attribute noise?\",\"answer\":\"Handling class noise using the DB technique improves MeBoTS performance for test case selection and code change request predictions, while handling attribute noise via a removal-based technique does not meaningfully improve MeBoTS performance.\"}]","Improving the Performance of Machine Learning-based Methods for Continuous Integration by Handling Noise - 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