Scenario Based Testing

Scenario-based testing systematically evaluates systems by exposing them to predefined scenarios, aiming to comprehensively assess performance and identify weaknesses before real-world deployment. Current research focuses on improving scenario generation using techniques like deep reinforcement learning, Bayesian networks, and contrastive learning to create realistic and diverse test cases, often leveraging real-world data and graph-based representations. This methodology is crucial for validating complex systems like autonomous vehicles and AI models, ensuring safety and reliability while reducing the cost and risk associated with real-world testing.