“Design And Develop Computational Models to Reduce the Maintenance Cost at Deployment Level”

Abstract

Software maintenance at the deployment stage remains a significant contributor to the total cost of ownership (TCO) in software engineering. Despite advanced development practices, many organizations experience escalated post-deployment maintenance due to unpredictable failures, inefficient resource utilization, and lack of intelligent monitoring systems. This paper presents the design and implementation of computational models that leverage machine learning and statistical methods to predict maintenance risks, automate diagnostics, and optimize resource allocation. Experimental results demonstrate a substantial reduction in maintenance efforts and costs when applied to real-world deployment environments. The proposed models offer scalable and intelligent solutions for enhancing software maintainability in production systems.