An Optimised Hybrid ELSVM-BRO Model for Predicting Software Reliability

An Optimised Hybrid ELSVM-BRO Model for Predicting Software Reliability | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article An Optimised Hybrid ELSVM-BRO Model for Predicting Software Reliability Suneel Kumar Rath, Madhusmita Sahu, Shom Prasad Das, Hrudaya Kumar Tripathy, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4590991/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 18 You are reading this latest preprint version Abstract In recent years, there has been a rise in strict environmental and safety regulations, resulting in the implementation of extra protocols dictating the functioning and state of software to effectively adhere to safety standards. As a result, the importance of timely, effective, and accurate maintenance procedures has grown significantly. Proper utilization of data has the potential to boost efficiency, reinforce safety measures, lower operational expenses, protect assets, enhance workforce productivity and advance environmental preservation efforts within the software industry. This research aims to devise a novel methodology capable of synchronizing data gathered from multiple sources and constructing a scalable framework to identify early indications of software malfunction. The proposed approach, explored in this study, integrates various Hybrid Extreme Learning Machine (ELM) and Support Vector Machine (SVM) with Binary Rao optimization (JAYA algorithm) techniques (ELSVM-BRO), directly evaluating time series data from the dataset. Pre-processing stages encompass data smoothing, filtering, outlier mitigation, and segmentation, followed by feature extraction for classification purposes. In the given context, a unique model is proposed. This model is a combination of Hybrid Extreme Learning and Support Vector Model, and it’s based on Binary Rao (BR) i.e., also known as Jaya Optimization. The primary purpose of this model is to evaluate the condition of a software system, specifically determining whether it’s faulty or healthy. Comparison with K-Nearest Neighbours (KNN), SVM, and Naïve Bayes (NB) and Random Forest (RF) classifiers using 10 datasets reveals that the ELSVM-BRO model attains superior balanced accuracy levels. The study suggests that amalgamating these algorithms enhances predictive reliability, particularly when applied to datasets of varying sizes. Software Reliability Prediction Multi-objective optimization Extreme Learning Machine Support Vector Machine Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 23 Sep, 2024 Reviewers agreed at journal 09 Sep, 2024 Reviews received at journal 06 Sep, 2024 Reviewers agreed at journal 05 Sep, 2024 Reviewers agreed at journal 05 Sep, 2024 Reviews received at journal 04 Sep, 2024 Reviewers agreed at journal 04 Sep, 2024 Reviews received at journal 03 Sep, 2024 Reviewers agreed at journal 03 Sep, 2024 Reviewers agreed at journal 03 Sep, 2024 Reviews received at journal 07 Aug, 2024 Reviews received at journal 29 Jul, 2024 Reviewers agreed at journal 29 Jul, 2024 Reviewers agreed at journal 29 Jul, 2024 Reviewers invited by journal 29 Jul, 2024 Editor assigned by journal 02 Jul, 2024 Submission checks completed at journal 25 Jun, 2024 First submitted to journal 16 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4590991","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":325243785,"identity":"a967c2f7-efa5-4a25-95e4-a137a2614b15","order_by":0,"name":"Suneel Kumar Rath","email":"","orcid":"","institution":"C.V. Raman Global University","correspondingAuthor":false,"prefix":"","firstName":"Suneel","middleName":"Kumar","lastName":"Rath","suffix":""},{"id":325243786,"identity":"51f19f7f-06f0-42a2-980a-b8ce76fc1fce","order_by":1,"name":"Madhusmita Sahu","email":"","orcid":"","institution":"C.V. 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