[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118215-en":3,"doc-seo-118215-105":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},118215,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Test Suite Optimization Using Machine Learning Techniques - A Comprehensive Study","Software testing remains a vital but expensive phase of the software development lifecycle, where test suite optimization aims to reduce execution cost while preserving strong fault detection. This study systematically reviews 43 machine-learning-based works from 2018 to 2023, spanning test case selection, prioritization, and reduction strategies. Results show supervised learning dominates current practice, while deep learning, hybrid models, and emerging Generative AI/LLM approaches offer improved scalability and fault detection potential. The review highlights trends, challenges, and future research directions.","Dakota State University  \nBeadle Scholar  \n\n| Research & Publications | Beacom College of Computer and Cyber Sciences |\n| --- | --- |\n\n2024  \nTest Suite Optimization Using Machine Learning Techniques: A Comprehensive Study  \nAbid Mehmood  \nQazi Mudassir Ilyas  \nMuneer Ahmad  \nZhongliang Shi  \nFollow this and additional works at: [https://scholar.dsu.edu/ccspapers](https://scholar.dsu.edu/ccspapers)  \nThis article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and content may change prior to final publication. Citation information: DOI 10. 1109/ACCESS.2024.3490453  \nTest Suite Optimization Using Machine  \nLearning Techniques: A Comprehensive Study  \nAbid Mehmood1, Qazi Mudassir Ilyas2, Muneer Ahmad3, and Zhongliang Shi4  \n1 The Beacom College of Computer & Cyber Sciences Dakota State University Madison, SD 57042, USA; ([abid.mehmood@dsu.edu](abid.mehmood@dsu.edu)) 2Department of Information Systems, College of Computer Sciences and Information Technology, King Faisal University, Al Ahsa 31982, Saudi Arabia; ([qilyas@kfu.edu.sa](qilyas@kfu.edu.sa))  \n3Department of Computer Science, University of Roehampton, Roehampton Lane, SW15 5PH, London, United Kingdom ([Muneer.Ahmad@roehampton.ac.uk](Muneer.Ahmad@roehampton.ac.uk))  \n4Jiangxi University of Software Professional Technology, China ([zhongliangshi@jxuspt.com](zhongliangshi@jxuspt.com))  \nCorresponding authors: Abid Mehmood (e-mail: [qilyas@kfu.edu.sa](qilyas@kfu.edu.sa)), Muneer Ahmad ([Muneer.Ahmad@roehampton.ac.uk](Muneer.Ahmad@roehampton.ac.uk)).  \n“The authors would like to thank Dakota State University, USA, for financially supporting the publication of this paper.”  \nABSTRACT Software testing is an essential yet costly phase of the software development lifecycle. While machine learning-based test suite optimization techniques have shown promise in reducing testing costs and improving fault detection, a comprehensive evaluation of their effectiveness across different environments is still lacking. This paper reviews 43 studies published between 2018 and 2023, covering various test caseselection, prioritization, and reduction techniques using machine learning. The findings reveal that conventional machine learning techniques, particularly supervised learning methods, have been widely adopted for test case prioritization and selection. Recent advancements, such as deep learning and hybrid models, show potential in improving fault detection rates and scalability, though challenges remain in adapting these techniques to large-scale and dynamic environments. Additionally, Generative AI and large language models (LLMs) are emerging as promising tools for automating aspects oftest case generation and prioritization, offering new avenues for future research in enhancing test suite optimization. The study identifies recent trends, challenges, and opportunities for further research, with a focus on both conventional and emerging methods, including deep learning, hybrid approaches, and Generative AI models. By systematically analyzing these techniques, this work contributes to the understanding of how machine learning and Generative AI can enhance test suite optimization and highlights future directions for improving the scalability and real-world applicability of these methods.  \nINDEX TERMS: Software Quality, Software Testing, Test Suite Optimization (TSO), Machine Learning in Software Testing, Test Case Selection; Evaluation Metrics for Test Suite Optimization  \nI. INTRODUCTION  \nSoftware development is a complex process that results in the creation of products and systems that influence our modern society. As technology advances at an unprecedented speed, there has never been a greater need for dependable, high-quality software. To address this demand, software development teams go through a rigorous and iterative process of designing, coding, and delivering software that satisfies consumers' demands. One of the mo","cbCaiplIwqUgfbHE","https://ap.wps.com/l/cbCaiplIwqUgfbHE","pdf",1038324,1,29,"English","en",105,"# Introduction\n# Test Suite Optimization (TSO)\n## Cost and resource constraints\n## Goal: efficient test coverage","[{\"question\":\"What problem does test suite optimization address in software testing?\",\"answer\":\"It selects a subset of test cases that provides comprehensive and efficient coverage, aiming to balance cost and fault detection effectiveness.\"},{\"question\":\"Which machine learning approaches are most commonly used in the reviewed studies?\",\"answer\":\"Conventional supervised learning techniques are widely adopted for test case prioritization and selection.\"},{\"question\":\"What newer technologies are discussed as promising for future research?\",\"answer\":\"Deep learning, hybrid models, and Generative AI/large language models (LLMs) are highlighted for automating parts of test case generation and prioritization.\"}]","Test Suite Optimization Using Machine Learning Techniques - A Comprehensive Study | PDF",1785682314,73,{"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},"test-suite-optimization-using-machine-learning-techniques-a-comprehensive-study","",{"@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/test-suite-optimization-using-machine-learning-techniques-a-comprehensive-study/118215/",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","2026-08-02",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 test suite optimization address in software testing?","Question",{"text":75,"@type":76},"It selects a subset of test cases that provides comprehensive and efficient coverage, aiming to balance cost and fault detection effectiveness.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning approaches are most commonly used in the reviewed studies?",{"text":80,"@type":76},"Conventional supervised learning techniques are widely adopted for test case prioritization and selection.",{"name":82,"@type":73,"acceptedAnswer":83},"What newer technologies are discussed as promising for future research?",{"text":84,"@type":76},"Deep learning, hybrid models, and Generative AI/large language models (LLMs) are highlighted for automating parts of test case generation and prioritization.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]