A Hybrid Graph–Markov Model for Workload Generation in Load Testing

Load testing is a critical component of performance engineering, but traditional script-based methodologies often fail to accurately represent the dynamic, stochastic behavior of real users in modern distributed systems. As web applications grow in complexity, linear testing sequences leave critical execution paths untested, obscuring concurrency bottlenecks. This paper proposes a hybrid conceptual framework that integrates probabilistic navigation graphs with Markov transition models to simulate realistic, chaotic user behavior. The proposed model represents application workflows as directed graphs, employing Markov chains to dictate virtual user navigation across system states based on probabilistic weights. By shifting from deterministic scripting to stochastic workload generation, the framework theoretically increases state space coverage and path diversity while providing a more flexible representation of user navigation behavior. We detail the multi-layered system architecture, formalize the mathematical foundation of the traversal engine, and introduce rigorous analytical metrics including transition entropy and state coverage probability. Ultimately, this framework introduces a probabilistic graph traversal approach that enables the stochastic exploration of application state spaces and emergent concurrency behavior.