Prediction of software faults using machine learning algorithms and mitigating risks with feature selection

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dc.authorscopusidFemilda Josephin Joseph Shobana Bai / 59417834100
dc.authorwosidFemilda Josephin Joseph Shobana Bai / JTQ-1812-2023
dc.contributor.authorBai, Femilda Josephin Joseph Shobana
dc.contributor.authorKaliraj S.
dc.contributor.authorUkrit, M. Ferni
dc.contributor.authorSivakumar V.
dc.date.accessioned2025-04-18T10:37:22Z
dc.date.available2025-04-18T10:37:22Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractSoftware fault prediction, a crucial component of software engineering, strives to detect probable flaws before they appear, thus enhancing the quality and reliability of software. Effective risk analysis is essential for reducing the risks and uncertainties that could arise during the development of software. The proposed work uses machine learning approaches to predict software faults and highlights the significance of risk analysis and feature selection. The accuracy of predictions can be increased by using feature selection approaches to help discover the features that strongly influence the prediction of software fault occurrence. The feature importance was identified by the algorithms using the decision trees (DT), gradient boosting machine (GBM), random forest (RF), and extreme gradient boosting (XGBoost) techniques. The models also underwent comparison by removing the features to understand the importance of the features and their correlation. Finally, a comparison is done to recognize the best model for software fault prediction.
dc.identifier.citationBai, F. J. J. S., Kaliraj, S., Ukrit, M. F., & Sivakumar, V. (2024). Prediction of software faults using machine learning algorithms and mitigating risks with feature selection. In Decision-Making Models (pp. 547-560). Academic Press.
dc.identifier.doi10.1016/B978-0-443-16147-6.00004-9
dc.identifier.endpage560
dc.identifier.isbn978-044316147-6
dc.identifier.isbn978-044316148-3
dc.identifier.scopus2-s2.0-85202894024
dc.identifier.startpage547
dc.identifier.urihttp://dx.doi.org/10.1016/B978-0-443-16147-6.00004-9
dc.identifier.urihttps://hdl.handle.net/20.500.12713/7148
dc.indekslendigikaynakScopus
dc.institutionauthorBai, Femilda Josephin Joseph Shobana
dc.institutionauthoridFemilda Josephin Joseph Shobana Bai / 0000-0003-0249-9506
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofDecision-making models: a perspective of fuzzy logic and machine learning
dc.relation.publicationcategoryKitap Bölümü - Uluslararası
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectDecision Tree
dc.subjectFeature Selection
dc.subjectGradient Boosting Machines
dc.subjectMachine Learning
dc.subjectRandom Forest
dc.subjectRisk Analysis
dc.subjectSoftware Fault Prediction
dc.titlePrediction of software faults using machine learning algorithms and mitigating risks with feature selection
dc.typeBook Chapter

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