Comparative analysis of Software Reliability using Grey Wolf Optimisation and Machine Learning

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dc.contributor.authorKelkar, S.
dc.contributor.authorVishvasrao, S.P.
dc.contributor.authorAgarwal, A.
dc.contributor.authorRajput, C.
dc.contributor.authorMohan, B.R.
dc.contributor.authorDas, M.
dc.date.accessioned2026-02-06T06:34:08Z
dc.date.issued2024
dc.description.abstractSoftware reliability is a crucial aspect of software quality. In this paper, we aim to explore the application of Gray Wolf Optimization (GWO) for feature selection and classification on various software dataset, such as KC1, JM1, and PC5. We compare the performance of Machine Learning models (Random Forest, Decision Tree, Support Vector Machine, XGBoost and Neural Networks) with and without GWO-based feature selection. Our results demonstrate the effectiveness of GWO in enhancing the accuracy of software reliability analysis. Or Math in Paper Title or Abstract. © 2024 IEEE.
dc.identifier.citation2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024, 2024, Vol., , p. -
dc.identifier.urihttps://doi.org/10.1109/IATMSI60426.2024.10503337
dc.identifier.urihttps://idr.nitk.ac.in/handle/123456789/29077
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.subjectBinary Classification
dc.subjectFeature Selection
dc.subjectGray Wolf Optimization (GWO)
dc.subjectSoftware Reliability Analysis
dc.titleComparative analysis of Software Reliability using Grey Wolf Optimisation and Machine Learning

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