[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127634-en":3,"doc-seo-127634-105":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},127634,962084925502,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Incorporating statistical and machine learning techniques into the optimization of correction factors for software development effort estimation","Accurate software development effort estimation supports efficient management by improving human resource planning and reducing project risk during early decision making. A stacking ensemble model is developed by integrating seven statistical and machine learning techniques and optimizing correction factors using grid search to determine valid search ranges and optimal configurations. Experiments compare the proposed approach with use case points-based, correction-factor-based, and single-method ensemble baselines across four datasets. Evaluation employs statistical tests and unbiased performance measures to confirm superior accuracy results.","Received: 2 November 2022 Revised: 13 June 2023 Accepted: 12 August 2023  \nDOI: 10.1002/smr.2611  \nRES EARCH A RTICLE - M ETHODOLOGY  \nIncorporating statistical and machine learning techniques into the optimization of correction factors for software development effort estimation  \nHo Le Thi Kim Nhung 1 | Vo Van Hai 2  | Petr Silhavy 3 | Zdenka Prokopova 3 | Radek Silhavy 3   \n1Faculty of Information Technology, University of Science–Vietnam National University, Ho Chi Minh City, Vietnam  \n2Faculty of Information Technology, Industrial University of Ho Chi Minh City, Ho Chi Minh City, Vietnam  \n3Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic  \nCorrespondence  \nRadek Silhavy, Tomas Bata University in Zlin, Faculty of Applied Informatics, Nad Stranemi 4511, 76001, Zlín, Czech Republic.  \nEmail: [rsilhavy@utb.cz](rsilhavy@utb.cz)  \nFunding information  \nTomas Bata University in Zlín, Grant/Award Numbers: RVO/FAI/2021/002, IGA/ CebiaTech/2022/001  \nAbstract  \nAccurate effort estimation is necessary for efficient management of software development projects, as it relates to human resource management. Ensemble methods, which employ multiple statistical and machine learning techniques, are more robust, reliable, and accurate effort estimation techniques. This study develops a stacking ensemble model based on optimization correction factors by integrating seven statistical and machine learning techniques (K-nearest neighbor, random forest, support vector regression, multilayer perception, gradient boosting, linear regression, and decision tree) . The grid search optimization method is used to obtain valid search ranges and optimal configuration values, allowing more accurate estimation. We conducted experiments to compare the proposed method with related methods, such as use case points-based single methods, optimization correction factors-based single methods, and ensemble methods. The estimation accuracies of the methods were evaluated using statistical tests and unbiased performance measures on a total of four datasets, thus demonstrating the effectiveness of the proposed method more clearly. The proposed method successfully maintained its estimation accuracy across the four experimental datasets and gave the best results in terms of the sum of squares errors, mean absolute error, root mean square error, mean balance relative error, mean inverted balance relative error, median of magnitude of relative error, and percentage of prediction (0 .25) . The p-value for the t-test showed that the proposed method is statistically superior to other methods in terms of estimation accuracy. The results show that the proposed method is a comprehensive approach for improving estimation accuracy and minimizing project risks in the early stages of software development.  \nKEYWOR DS  \noptimizing correction factors, software development effort estimation, staked generalization ensemble, statistical and machine learning techniques  \nThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.  \n© 2023 The Authors. Journal of Software: Evolution and Process published by John Wiley & Sons Ltd.  \nJ Softw Evol Proc. 2023;e2611 .  \n[https://doi.org/10.1002/smr.2611](https://doi.org/10.1002/smr.2611)  \n[wileyonlinelibrary.com/journal/smr](wileyonlinelibrary.com/journal/smr)  \n1 of 37  \n2 of 37  \nNHUNG  \nET AL.  \n1 | INTRODUCTION  \nThe complexity of software project development has increased, and this industry demands a high level of competence from its employees, who must possess particular skills. Project managers typically need such early estimates to bid on a project contract and make informed planning decisions.1 However, they often encounter difficulties in estimating effort, cost, and schedule correctly in advance. Customer ","cbCaioUSondJHaQL","https://ap.wps.com/l/cbCaioUSondJHaQL","pdf",5818380,1,37,"English","en",105,"# Abstract\n## Method Overview: Stacking Ensemble and Correction Factors Optimization\n## Experimental Design and Comparison\n## Evaluation: Statistical Tests and Unbiased Measures\n## Results and Implications for Early-Stage Risk Reduction","[{\"question\":\"What problem does the paper address in software engineering management?\",\"answer\":\"The paper addresses the challenge of accurately estimating effort, cost, and schedule in the early stages of software development, where uncertainty and incomplete requirements are common.\"},{\"question\":\"How does the proposed method improve effort estimation accuracy?\",\"answer\":\"It builds a stacking ensemble that integrates seven statistical and machine learning techniques and uses grid search to optimize correction factor configurations for more accurate estimation.\"},{\"question\":\"What evidence is used to show the method’s effectiveness?\",\"answer\":\"The study evaluates methods on four datasets using statistical tests and multiple unbiased performance measures, and the t-test indicates the proposed approach is statistically superior in estimation accuracy.\"}]","Incorporating statistical and machine learning techniques into the optimization of correction factors for software development effort estimation | 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problem does the paper address in software engineering management?","Question",{"text":75,"@type":76},"The paper addresses the challenge of accurately estimating effort, cost, and schedule in the early stages of software development, where uncertainty and incomplete requirements are common.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed method improve effort estimation accuracy?",{"text":80,"@type":76},"It builds a stacking ensemble that integrates seven statistical and machine learning techniques and uses grid search to optimize correction factor configurations for more accurate estimation.",{"name":82,"@type":73,"acceptedAnswer":83},"What evidence is used to show the method’s effectiveness?",{"text":84,"@type":76},"The study evaluates methods on four datasets using statistical tests and multiple unbiased performance measures, and the t-test indicates the proposed approach is statistically superior in estimation 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