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The core elements of our proposed stacked ensemble strategy include Decision Tree, Principal Components Regression, Random Forest, NeuralNet, GLMNET, XGBoost, Earth, and Support Vector Machine. Moreover, we augment the model\u2019s performance by incorporating a blend of these foundational algorithms with other ensemble regression methods. Extensive testing in the suggested research work with a number of Super Learners demonstrates that Regression is the best technique for judging effort. The evaluation of the different estimators involved the use of various metrics, including Mean Absolute Error, Root Mean Squared Error, Mean Squared Error, Percentage of Close Approximations within 25% of the True Values (PRED (25)), R-Squared Coefficients, Precision, Recall, and F1-Score. The proposed method yields more trustworthy predicted performance than either single-model approaches or stacked ensembles. Effort estimation serves as the foundation for the rest of the project management process.<\/jats:p>","DOI":"10.3233\/jifs-230676","type":"journal-article","created":{"date-parts":[[2023,9,5]],"date-time":"2023-09-05T11:48:23Z","timestamp":1693914503000},"page":"9697-9713","source":"Crossref","is-referenced-by-count":1,"title":["Software effort estimation using stacked ensembled techniques and proposed stacking ensemble using principal component regression as super learner"],"prefix":"10.1177","volume":"45","author":[{"given":"A.G.","family":"Priya Varshini","sequence":"first","affiliation":[{"name":"IT Department, Dr. Mahalingam College of Engineering and Technology, Pollachi, India"}]},{"given":"K.","family":"Anitha Kumari","sequence":"additional","affiliation":[{"name":"IT Department, PSG College of Technology, Coimbatore, India"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-230676_ref1","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.ijcce.2021.10.003","article-title":"Tackling requirements uncertainty in software projects: a cognitive approach","volume":"2","author":"Haleem","year":"2021","journal-title":"International Journal of Cognitive Computing in Engineering"},{"key":"10.3233\/JIFS-230676_ref2","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.infsof.2006.05.001","article-title":"State of the practice: An exploratory analysis of schedule estimation and software project success prediction","volume":"49.2","author":"Verner","year":"2007","journal-title":"Information and Software Technology"},{"key":"10.3233\/JIFS-230676_ref3","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1016\/j.ijforecast.2007.05.008","article-title":"Forecasting of software development work effort: Evidence on expert judgement and formal models","volume":"23.3","author":"J\u00f8rgensen","year":"2007","journal-title":"International Journal of Forecasting"},{"key":"10.3233\/JIFS-230676_ref4","doi-asserted-by":"crossref","first-page":"1770","DOI":"10.1016\/j.jss.2007.03.001","article-title":"Inconsistency of expert judgment-based estimates of software development effort","volume":"80.11","author":"Grimstad","year":"2007","journal-title":"Journal of Systems and Software"},{"key":"10.3233\/JIFS-230676_ref5","first-page":"1468","article-title":"Analogy-Based Approaches to Improve Software Project Effort Estimation Accuracy","volume":"29.1","author":"Resmi","year":"2020","journal-title":"Journal of Intelligent Systems"},{"key":"10.3233\/JIFS-230676_ref6","doi-asserted-by":"crossref","first-page":"106330","DOI":"10.1016\/j.infsof.2020.106330","article-title":"On an optimal analogy-based software effort estimation","volume":"125","author":"Phannachitta","year":"2020","journal-title":"Information and Software Technology"},{"key":"10.3233\/JIFS-230676_ref7","doi-asserted-by":"crossref","first-page":"807","DOI":"10.1142\/S0218194016500261","article-title":"Regression analysis based software effort estimation method","volume":"26.05","author":"Y\u00fccalar","year":"2016","journal-title":"International Journal of Software Engineering and Knowledge Engineering"},{"key":"10.3233\/JIFS-230676_ref8","doi-asserted-by":"crossref","first-page":"2094","DOI":"10.17485\/IJST\/v13i21.573","article-title":"Predictive analytics approaches for software effort estimation: A review","volume":"13","author":"Priya Varshini","year":"2020","journal-title":"Indian J. 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