{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:03:01Z","timestamp":1760058181311,"version":"build-2065373602"},"reference-count":84,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,3,21]],"date-time":"2025-03-21T00:00:00Z","timestamp":1742515200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Software"],"abstract":"<jats:p>The strategic significance of software testing in ensuring the success of software development projects is paramount. Comprehensive testing, conducted early and consistently across the development lifecycle, is vital for mitigating defects, especially given the constraints on time, budget, and other resources often faced by development teams. Software defect prediction (SDP) serves as a proactive approach to identifying software components that are most likely to be defective. By predicting these high-risk modules, teams can prioritize thorough testing and inspection, thereby preventing defects from escalating to later stages where resolution becomes more resource intensive. SDP models must be continuously refined to improve predictive accuracy and performance. This involves integrating clean and preprocessed datasets, leveraging advanced machine learning (ML) methods, and optimizing key metrics. Statistical-based and traditional ML approaches have been widely explored for SDP. However, statistical-based models often struggle with scalability and robustness, while conventional ML models face challenges with imbalanced datasets, limiting their prediction efficacy. In this study, innovative decision forest (DF) models were developed to address these limitations. Specifically, this study evaluates the cost-sensitive forest (CS-Forest), forest penalizing attributes (FPA), and functional trees (FT) as DF models. These models were further enhanced using homogeneous ensemble techniques, such as bagging and boosting techniques. The experimental analysis on benchmark SDP datasets demonstrates that the proposed DF models effectively handle class imbalance, accurately distinguishing between defective and non-defective modules. Compared to baseline and state-of-the-art ML and deep learning (DL) methods, the suggested DF models exhibit superior prediction performance and offer scalable solutions for SDP. Consequently, the application of DF-based models is recommended for advancing defect prediction in software engineering and similar ML domains.<\/jats:p>","DOI":"10.3390\/software4020007","type":"journal-article","created":{"date-parts":[[2025,3,21]],"date-time":"2025-03-21T10:10:08Z","timestamp":1742551808000},"page":"7","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Empirical Analysis of Data Sampling-Based Decision Forest Classifiers for Software Defect Prediction"],"prefix":"10.3390","volume":"4","author":[{"given":"Fatima Enehezei","family":"Usman-Hamza","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Ilorin, Ilorin 1515, Nigeria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7411-3639","authenticated-orcid":false,"given":"Abdullateef Oluwagbemiga","family":"Balogun","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Bandar Seri Iskandar 32610, Perak, Malaysia"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-0404-3125","authenticated-orcid":false,"given":"Hussaini","family":"Mamman","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Bandar Seri Iskandar 32610, Perak, Malaysia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6966-2369","authenticated-orcid":false,"given":"Luiz Fernando","family":"Capretz","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Western University, London, ON N6A 5B9, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1736-4834","authenticated-orcid":false,"given":"Shuib","family":"Basri","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Bandar Seri Iskandar 32610, Perak, Malaysia"}]},{"given":"Rafiat Ajibade","family":"Oyekunle","sequence":"additional","affiliation":[{"name":"Department of Information Technology, University of Ilorin, Ilorin 1515, Nigeria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2389-7039","authenticated-orcid":false,"given":"Hammed Adeleye","family":"Mojeed","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Ilorin, Ilorin 1515, Nigeria"},{"name":"Department of Technical Informatics and Telecommunications, Gda\u0144sk University of Technology, Gabriela Narutowicza 11\/12, 80-233 Gda\u0144sk, Poland"}]},{"given":"Abimbola Ganiyat","family":"Akintola","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Ilorin, Ilorin 1515, Nigeria"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Laplante, P.A., and Kassab, M. 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