[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121653-en":3,"doc-seo-121653-105":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},121653,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Applying Machine Learning Analysis for Software Quality Test","Software maintenance is a major cost, so anticipating which factors lead to maintenance problems is essential. The paper addresses residual defectiveness forecasting by applying machine learning to available data: it extracts software metrics from static source code and uses reported repository bugs as defect information. A correlation-based filtering step removes metrics unrelated to defects, enabling analysis of full datasets without stopping development. The objective is accurate prediction of software defects and reduced maintenance costs.","Applying Machine Learning Analysis for  \nSoftware Quality Test  \narXiv :2305 .09695v1 [ cs . SE] 16 May 2023  \nAl Khan Remudin Reshid Mekuria  \nRuslan Isaev  \n[pkhan_1@hotmail.com](pkhan_1@hotmail.com)[ ](pkhan_1@hotmail.com)[remudin@alatoo.edu.kg](remudin@alatoo.edu.kg)[ ](remudin@alatoo.edu.kg)[ruslan.isaev@alatoo.edu.kg](ruslan.isaev@alatoo.edu.kg)[ ](ruslan.isaev@alatoo.edu.kg)Ala-Too International University Bishkek, Kyrgyzstan  \nAbstract  \nOne of the biggest expense in software development is the maintenance. Therefore, it’s critical to comprehend what triggers maintenance and if it may be predicted. Numerous research outputs have demonstrated that specific methods of assessing the complexity of created programs may produce useful prediction models to ascertain the possibility of maintenance due to software failures. As a routine it is performed prior to the release, and setting up the models frequently calls for certain, object-oriented software measurements. It’s not always the case that software developers have access to these measurements. In this paper, machine learning is applied on the available data to calculate the cumulative software failure levels. A technique to forecast a software’s residual defectiveness using machine learning can be looked into as a solution to the challenge of predicting residual flaws. Software metrics and defect data were separated out of the static source code repository. Static code is used to create software metrics, and reported bugs in the repository are used to gather defect information. By using a correlation method, metrics that had no connection to the defect data were removed. This makes it possible to analyze all the data without pausing the programming process. Large, sophisticated software’s primary issue is that it is impossible to control everything manually, and the cost of an error can be quite expensive. Developers may miss errors during testing as a consequence, which will raise maintenance costs. Finding a method to accurately forecast software defects is the overall objective.  \nKeywords: Machine learning, correlation method, metrics, defect prediction, cumulative failure prediction  \n1 Introduction  \nA software feature is a functional component [1] . Creating software is frequently centered on creating features. Development of features is carried out such that one or more features are not created by several teams simultaneously. Errors in program are frequently unevenly distributed throughout all of its components or features [33] . Instead, certain features have a tendency to fail more frequently than others. The next issue is whether it is possible to detect these errors before they start to result in failures. If so, this may make it possible to examine these characteristics more closely before implementing for the clients. It is a general accepted fact that defects are unavoidable when designing large-scale software. Therefore, it is crucial for when creating software to have a way to address flaws as they evolve into failures. Failures that aren’t managed well can cost a lot of money and damage your reputation with customers. While proactive handling in particular and competent failure management in general may cut costs and boost customer trust. How may the repercussions of software failures be decreased? For this, two strategies are taken into account. First, determine how high a release’s failure rate is expected to be. And second, by narrowing the focus to identify which software features are more prone to errors in a future release. This demands for a system that can forecast impending failures and predict developed software features according to how likely they are to lead to mistakes. Such a system ought tobe impartial, clear, and accurate (to the extent that is possible and feasible) . This is necessary because it must be simple to compare outcomes across the organization, simple to deploy, and useful. It may be argued that machine learning methods are","cbCaitGv8QHTE2MP","https://ap.wps.com/l/cbCaitGv8QHTE2MP","pdf",1085616,1,16,"English","en",105,"# Introduction\n## Software features and defect concentration\n## Proactive failure management\n## Defect forecasting using machine learning\n## Motivation from unit size and defect relations","[{\"question\":\"Why is forecasting software failures and residual defects important?\",\"answer\":\"Maintenance costs are high, and unmanaged failures can damage reputation and increase expenses. Forecasting impending failures helps identify error-prone features before they cause failures.\"},{\"question\":\"How does the paper build data for machine learning defect prediction?\",\"answer\":\"Software metrics are derived from static source code in a repository, while defect information comes from reported bugs stored in the same repository.\"},{\"question\":\"What is the role of the correlation method in the workflow?\",\"answer\":\"The correlation method identifies and removes metrics that have no connection to defect data, improving the usefulness of the remaining features.\"}]","Applying Machine Learning Analysis for Software Quality Test | PDF",1785805962,40,{"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},"applying-machine-learning-analysis-for-software-quality-test","",{"@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/applying-machine-learning-analysis-for-software-quality-test/121653/",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","2026-08-04",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},"Why is forecasting software failures and residual defects important?","Question",{"text":75,"@type":76},"Maintenance costs are high, and unmanaged failures can damage reputation and increase expenses. Forecasting impending failures helps identify error-prone features before they cause failures.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the paper build data for machine learning defect prediction?",{"text":80,"@type":76},"Software metrics are derived from static source code in a repository, while defect information comes from reported bugs stored in the same repository.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the role of the correlation method in the workflow?",{"text":84,"@type":76},"The correlation method identifies and removes metrics that have no connection to defect data, improving the usefulness of the remaining features.","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},[93,97,101,105,110,115,119,122,127,130,134],{"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":29,"slug":118},7,"Healthcare","healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]