Hybrid Genetic Algorithm and Machine Learning Approach for Software Reliability Assessment in Safety-Critical Systems

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dc.contributor.authorGoyal, G.
dc.contributor.authorSharma, K.
dc.contributor.authorAnshuman
dc.contributor.authorMittal, V.
dc.contributor.authorSingla, B.
dc.contributor.authorDas, M.
dc.contributor.authorMohan, B.R.
dc.date.accessioned2026-02-06T06:34:08Z
dc.date.issued2024
dc.description.abstractSoftware reliability is a paramount determinant of software quality. In this research paper, we delve into utilizing Genetic Algorithms (GAs) for feature selection and classification. We undertake a comprehensive evaluation and comparative analysis of Machine Learning models, specifically Random Forest and Logistic Regression, both with and without Genetic Algorithmdriven feature selection. Our findings substantiate the significant impact of Genetic Algorithms in improving the accuracy of software reliability analysis. © 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/IATMSI60426.2024.10503224
dc.identifier.urihttps://idr.nitk.ac.in/handle/123456789/29073
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.subjectBio-Optimization
dc.subjectChi-Squared Statistic
dc.subjectGenetic Algorithms
dc.subjectLogistic Regression
dc.subjectMachine Learning
dc.subjectModel Evaluation
dc.subjectPrincipal Component Analysis
dc.subjectRandom Forest
dc.subjectSoftware Reliability Analysis
dc.titleHybrid Genetic Algorithm and Machine Learning Approach for Software Reliability Assessment in Safety-Critical Systems

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