[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126064-en":3,"doc-seo-126064-105":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},126064,962084931830,"Theodore","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",6,"Technology","Using AI and Machine Learning in QA Testing - Article","The article examines practical possibilities of applying artificial intelligence and machine learning in software quality control, focusing on how these technologies can reshape testing approaches. It covers methods that increase QA effectiveness, including test automation, defect detection, and anomaly prediction. The methodology synthesizes achievements from scientific papers, highlighting adaptive algorithms for generating tests, clustering techniques for systematizing errors, and big-data analysis for predicting defects. Case comparisons of manual UI testing versus automated regression tests support measurable gains in speed, reduced error leakage, and improved process quality.","Using AI and Machine Learning in QA Testing  \nNikita Klimova*  \naSenior QA / QC Engineer in ADP inc.Miami, United States  \nAbstract  \nThe article will consider the possibilities of using artificial intelligence (AI) and machine learning (ML) technologies in the field of software quality control due to the fact that they are able to change the usual approaches to testing due to their abilities. Methods of using AI and ML to increase the effectiveness of quality assurance (QA) will be considered: automation of tests, detection of defects, prediction of anomalies. The methodology is based on the analysis of scientific papers, which will describe achievements in the application of these technologies during QA, including adaptive algorithms that automatically generate tests, clustering methods that systematize errors, and big data analysis that allows predicting defects. As part of the work, examples of organizations that demonstrate comparing user interface testing using a manual method and automated regression tests will also be considered. The data obtained show a decrease in the time spent on testing, a decrease in the probability of missing errors, and an improvement in the quality of processes. The information in the work will be useful to quality specialists, developers, and AI researchers working on optimizing testing. In conclusion, the article notes the success of applying such technological solutions in achieving QA goals.  \nKeywords: Artificial Intelligence, Machine Learning, QA Testing, Automation, Defect Prediction, Anomaly Analysis.  \n1. Introduction  \nIn the context of evolving realities, the process of software development is being reshaped by the capabilities provided by AI and ML. The task is not merely to accelerate or automate testing but to provide adaptive mechanisms for comprehensive analysis and defect prediction. Traditional testing approaches currently reveal several vulnerabilities, especially as software architectures scale and become more complex. Firstly, they exhibit limited flexibility in adapting to dynamically changing parameters and interactions within complex systems. Secondly, the human factor introduces a risk of oversights and leads to excessive costs associated with routine processes.  \nReceived: 4/18/2023  \nPublished: 4/18/2023  \n* Corresponding author.  \nA significant portion of repetitive tasks, such as regression testing, consumes the time of specialists, making them susceptible to errors. Thus, the application of AI and ML in QA enhances the accuracy of testing. These algorithms are capable of analyzing large datasets, recognizing patterns, and predicting defects, thereby minimizing the likelihood of oversights. Such methods accelerate the testing process and make it more cost-effective.  \nConversely, approaches based on rigidly fixed test scenarios do not account for stochastic changes, such as variability or dependencies between systems, thereby limiting the ability to timely detect hidden defects and deviations [2] .  \nThe relevance of this topic has grown alongside the increasing number of products incorporating AI and ML components, which require particularly thorough testing to ensure reliability, safety, and compliance with stated requirements. Their integration into QA optimizes testing processes and expands functional capabilities, including prediction and diagnostics, ultimately improving the overall quality of software.  \nThe aim of this study is to examine the current AI and ML methods and algorithms used in QA and to analyze their effectiveness in enhancing software quality.  \n2. Materials and Methods  \nSeveral scientific methods were employed in this study. The analytical method enabled a comprehensive review of current approaches to applying AI and ML in QA. The comparative analysis method was used to evaluate the effectiveness of various algorithms and tools utilized in automated testing, including classification methods, anomaly detection, and test data generation.  \nTh","cbCaifww8Ykf0IZr","https://ap.wps.com/l/cbCaifww8Ykf0IZr","pdf",204642,12,1,7,"English","en",105,"# Introduction\n# Materials and Methods","[{\"question\":\"What does the study aim to accomplish in AI/ML QA testing?\",\"answer\":\"The study reviews current AI and ML methods and algorithms used in QA and analyzes how effectively they improve software quality.\"},{\"question\":\"Which QA improvement methods are discussed in the article?\",\"answer\":\"The article considers test automation, defect detection, and prediction of anomalies, supported by adaptive algorithms, clustering for error systematization, and big-data analysis for defect prediction.\"},{\"question\":\"Why are traditional testing approaches described as insufficient?\",\"answer\":\"Traditional methods struggle with dynamically changing parameters and interactions in complex systems, and human-driven routine work can introduce oversights and increase costs.\"}]","Using AI and Machine Learning in QA Testing - Article | PDF",1785902869,18,{"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},"using-ai-and-machine-learning-in-qa-testing-article","",{"@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/technology/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/using-ai-and-machine-learning-in-qa-testing-article/126064/",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","2026-08-24","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 does the study aim to accomplish in AI/ML QA testing?","Question",{"text":77,"@type":78},"The study reviews current AI and ML methods and algorithms used in QA and analyzes how effectively they improve software quality.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Which QA improvement methods are discussed in the article?",{"text":82,"@type":78},"The article considers test automation, defect detection, and prediction of anomalies, supported by adaptive algorithms, clustering for error systematization, and big-data analysis for defect prediction.",{"name":84,"@type":75,"acceptedAnswer":85},"Why are traditional testing approaches described as insufficient?",{"text":86,"@type":78},"Traditional methods struggle with dynamically changing parameters and interactions in complex systems, and human-driven routine work can introduce oversights and increase costs.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,112,115,119,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":110,"slug":111},5,"Comic",60,"comic",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":113,"slug":114},50,"technology",{"id":22,"doc_module":4,"doc_module_name":47,"category_name":116,"show_sort_weight":117,"slug":118},"Healthcare",40,"healthcare",{"id":120,"doc_module":4,"doc_module_name":47,"category_name":121,"show_sort_weight":122,"slug":123},8,"Research & Report",30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":108,"slug":139},19,"General","general"]