Application of Mathematical Modeling in Test Case Coverage Analysis for Training Management Platforms

Abstract

With the proliferation of educational technology platforms, training management systems have become essential infrastructure for higher education institutions worldwide. These platforms facilitate complex workflows including student internship applications, supervisor assignments, and performance evaluations, necessitating comprehensive quality assurance methodologies to ensure system reliability and user satisfaction. This study investigates the application of mathematical modeling techniques to enhance test case coverage analysis in training management platforms, drawing upon real-world implementation data from comprehensive student training systems. Through the development of probabilistic analysis models, stochastic frameworks, and optimization algorithms, this research provides systematic methodologies for improving software testing efficiency and effectiveness. The proposed mathematical models demonstrate substantial improvements in test coverage accuracy while offering theoretical foundations and practical guidance for quality assurance in educational software systems. Experimental validation reveals that the mathematical modeling approach achieves superior performance compared to traditional testing methods, with significant implications for software quality engineering in educational environments.