[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-290750-105":3,"doc-detail-290750-en":81,"detail-sidebar-cat-0-en-105":97},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","software-effort-estimation-using-machine-learning-technique","Software Effort Estimation using Machine Learning Technique","","Software engineering effort estimation is central to managing software project cost, quality, and schedule. Over recent decades, extensive research has applied diverse machine learning techniques to improve prediction effectiveness. This study recommends multiple algorithms for effort estimation, including k-nearest neighbor regression, support vector regression, and decision trees, addressing limitations of parametric and conventional estimation approaches. A dataset created by Edusoft Consultants Ltd is used for evaluation with mean absolute error (MAE), mean squared error (MSE), and R square measures. Experimental comparisons show decision trees provide superior effort prediction.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/software-effort-estimation-using-machine-learning-technique/290750/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/software-effort-estimation-using-machine-learning-technique/290750.png","ImageObject",300,407,{"name":42,"@type":43},"Riley","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-27","2026-09-17",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",15,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"Which machine learning algorithms are used for software effort estimation?","Question",{"text":63,"@type":64},"The study evaluates k-nearest neighbor regression, support vector regression, and decision trees for estimating software engineering effort.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"What performance evaluation measures are used in the comparison?",{"text":68,"@type":64},"The paper uses mean absolute error (MAE), mean squared error (MSE), and R square as common performance evaluation measures.",{"name":70,"@type":61,"acceptedAnswer":71},"How does the study’s dataset affect the evaluation?",{"text":72,"@type":64},"The dataset created by Edusoft Consultants Ltd is used to assess the effectiveness of the proposed estimation methods.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},290750,1789642502,{"code":4,"msg":82,"data":83},"success",{"doc_id":79,"user_id":84,"nickname":42,"user_avatar":85,"doc_module":4,"category_id":86,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":87,"file_id":88,"file_url":89,"file_type":90,"file_size":91,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":26,"language":92,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":93,"faqs":94,"seo_title":95,"seo_description":12,"update_tm":80,"read_time":96,"preview_page_count":4},1374391975076,"https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Software effort estimation using machine learning technique  \nRahman, Mizanura; Roy, Partha Protimb; Ali, Mohammadc; Gonçalves, Teresad; Sarwar, Hasanea Faculty of Computing, Universiti Malaysia Pahang, Pahang, Pekan, 26600, Malaysia  \nb United International University, Dhaka, Bangladesh  \nc Edusoft Consultants Ltd, Dhaka, Bangladesh d Department of Computer Science, University of Évora, Portugal e Department of Computer Science and Engineering, United International University,  \nSatarkul, Badda,Dhaka, Bangladesh  \nABSTRACT  \nSoftware engineering effort estimation plays a significant role in managing project cost, quality, and time and creating software. Researchers have been paying close attention to software estimation during the past few decades, and a great amount of work has been done utilizing a variety of machinelearning techniques and algorithms. In order to better effectively evaluate predictions, this study recommends various machine learning algorithms for estimating, including k-nearest neighbor regression, support vector regression, and decision trees. These methods are now used by the software development industry for software estimating with the goal of overcoming the limitations of parametric and conventional estimation techniques and advancing projects. Our dataset, which was created by a software company called Edusoft Consulted LTD, was used to assess the effectiveness of the established method. The three commonly used performance evaluation measures, mean absolute error (MAE), mean squared error (MSE), and R square error, represent the base for these. Comparative experimental results demonstrate that decision trees perform better at predicting effort than other techniques  \nKEYWORDS  \nDecision tree; K-nearest neighbor regression; Machine learning; Software effort estimation; Support vector regression  \nACKNOWLEDGEMENT  \nThe work reported in this paper is funded by the Institute for Advanced Research (IAR), United International University (UIU), Bangladesh titled: Implementation of 3D model to assess the performance improvement of a software company UIU/IAR/02/2019-20/SE/06 .","cbCait7bH3AFoqbN","https://ap.wps.com/l/cbCait7bH3AFoqbN","pdf",270294,"English","# Abstract\n## Algorithms for effort estimation\n## Dataset and evaluation metrics\n## Experimental results","[{\"question\":\"Which machine learning algorithms are used for software effort estimation?\",\"answer\":\"The study evaluates k-nearest neighbor regression, support vector regression, and decision trees for estimating software engineering effort.\"},{\"question\":\"What performance evaluation measures are used in the comparison?\",\"answer\":\"The paper uses mean absolute error (MAE), mean squared error (MSE), and R square as common performance evaluation measures.\"},{\"question\":\"How does the study’s dataset affect the evaluation?\",\"answer\":\"The dataset created by Edusoft Consultants Ltd is used to assess the effectiveness of the proposed estimation methods.\"}]","Software Effort Estimation using Machine Learning Technique | PDF",5,{"code":4,"msg":82,"data":98},[99,103,107,111,115,120,125,128,133,136,140],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":100,"show_sort_weight":101,"slug":102},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":104,"show_sort_weight":105,"slug":106},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":108,"show_sort_weight":109,"slug":110},"Exam",70,"exam",{"id":96,"doc_module":4,"doc_module_name":25,"category_name":112,"show_sort_weight":113,"slug":114},"Comic",60,"comic",{"id":116,"doc_module":4,"doc_module_name":25,"category_name":117,"show_sort_weight":118,"slug":119},6,"Technology",50,"technology",{"id":121,"doc_module":4,"doc_module_name":25,"category_name":122,"show_sort_weight":123,"slug":124},7,"Healthcare",40,"healthcare",{"id":86,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":126,"slug":127},30,"research-report",{"id":129,"doc_module":4,"doc_module_name":25,"category_name":130,"show_sort_weight":131,"slug":132},9,"Religion & Spirituality",20,"religion-spirituality",{"id":131,"doc_module":4,"doc_module_name":25,"category_name":134,"show_sort_weight":131,"slug":135},"World Cup","world-cup",{"id":137,"doc_module":4,"doc_module_name":25,"category_name":138,"show_sort_weight":137,"slug":139},10,"Lifestyle","lifestyle",{"id":141,"doc_module":4,"doc_module_name":25,"category_name":142,"show_sort_weight":96,"slug":143},19,"General","general"]