A Novel Software Reliability Growth Model Based on Stochastic Differential Equations for Open-Source Software

A Novel Software Reliability Growth Model Based on Stochastic Differential Equations for Open-Source Software | 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 A Novel Software Reliability Growth Model Based on Stochastic Differential Equations for Open-Source Software Fatimah Saad Hameed, Hussein K. Asker This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5494782/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In this study, we discussed the reliability assessment for Open Source Software by introducing a new software reliability growth model based on stochastic differential equations. We considered that the software failure rate depends on time (t), and the reporting of faults is irregular. First, we determined its probability distribution by transforming the fundamental differential equation describing fault detection into a stochastic differential equation of Itˆo type. Second, we calculated several software reliability measures, such as the expected number of faults and the mean time between cumulative and instantaneous software failures. Using data from actual software testing procedures, we used the maximum likelihood method to estimate the model’s unknown parameters. Finally, we compared the new model with two other reliability growth models (the exponential and the delayed S-shaped), using a set of software quality standards to identify the best model. The results demonstrated that the proposed model can enhance the quality of OSS systems. Non homogeneous Poisson process (NHPP) Open source software (OSS) Software reliability growth model (SRGM) Stochastic differential equations (SDE). Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted 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-5494782","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":380729475,"identity":"04a3fee8-b055-471a-8a46-43b4170ac49b","order_by":0,"name":"Fatimah Saad Hameed","email":"","orcid":"","institution":"University of Kufa","correspondingAuthor":false,"prefix":"","firstName":"Fatimah","middleName":"Saad","lastName":"Hameed","suffix":""},{"id":380729476,"identity":"c6a9ee56-a330-40bc-ac57-827d9c1429a0","order_by":1,"name":"Hussein K. 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We considered that the software failure rate depends \u0026nbsp;on time (t), and the reporting of faults is irregular. First, we determined its probability distribution by transforming the \u0026nbsp;fundamental differential equation describing fault detection into a stochastic differential equation of Itˆo type. Second, we \u0026nbsp;calculated several software reliability measures, such as the expected number of faults and the mean time between \u0026nbsp;cumulative and instantaneous software failures. Using data from actual software testing procedures, we used the \u0026nbsp;maximum likelihood method to estimate the model’s unknown parameters. 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