Neha, Reddy and Jonathan, Miller and Dr. Ali Hassan, Al-Tamimi (2024) Predictive Maintenance in Software Systems: Leveraging AI and Data Science to Reduce Failures in Continuous Deployment Environments. International Journal of Informatics and Data Science Research, 1 (9). pp. 72-89. ISSN 2997-3961

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Abstract

The increasing reliance on continuous integration and continuous deployment (CI/CD) pipelines in modern software engineering has amplified the risk of unexpected system failures, service downtime, and security vulnerabilities. Traditional maintenance approaches, which rely on reactive or scheduled interventions, are insufficient in highly dynamic environments where rapid code changes and microservices architectures dominate. Predictive maintenance, powered by artificial intelligence (AI) and data science, offers a transformative alternative by anticipating failures before they occur and enabling proactive interventions.

Item Type:	Article
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Postgraduate > Master's of Islamic Education
Depositing User:	Journal Editor
Date Deposited:	03 Oct 2025 12:25
Last Modified:	03 Oct 2025 12:25
URI:	http://eprints.umsida.ac.id/id/eprint/16407

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