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In this study, we evaluated and compared various machine learning models, including logistic regression (LR), random forest (RF), support vector machines (SVMs), convolutional neural networks (CNNs), and eXtreme Gradient Boosting (XGBoost), for software defect prediction using a combination of diverse datasets. The models were trained and tested on preprocessed and feature\u2010selected data, followed by optimization through hyperparameter tuning. Performance evaluation metrics were employed to analyze the results comprehensively, including classification reports, confusion matrices, receiver operating characteristic\u2013area under the curve (ROC\u2010AUC) curves, precision\u2013recall curves, and cumulative gain charts. The results revealed that XGBoost consistently outperformed other models, achieving the highest accuracy, precision, recall, and AUC scores across all metrics. This indicates its robustness and suitability for predicting software defects in real\u2010world applications.<\/jats:p>","DOI":"10.1049\/sfw2\/8832164","type":"journal-article","created":{"date-parts":[[2025,5,24]],"date-time":"2025-05-24T07:34:29Z","timestamp":1748072069000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Predicting Software Perfection Through Advanced Models to Uncover and Prevent Defects"],"prefix":"10.1049","volume":"2025","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5718-5585","authenticated-orcid":false,"given":"Tariq","family":"Shahzad","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sunawar","family":"Khan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4649-2376","authenticated-orcid":false,"given":"Tehseen","family":"Mazhar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wasim","family":"Ahmad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8462-5061","authenticated-orcid":false,"given":"Khmaies","family":"Ouahada","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5320-1012","authenticated-orcid":false,"given":"Habib","family":"Hamam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"265","published-online":{"date-parts":[[2025,5,24]]},"reference":[{"key":"e_1_2_12_1_2","article-title":"Artificial Intelligence for Self-Healing Automation Testing Frameworks: Real-Time Fault Prediction and Recovery","volume":"64","author":"Prathyusha N.","year":"2024","journal-title":"Artificial Intelligence"},{"key":"e_1_2_12_2_2","unstructured":"ZezhouY. 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