Test Data Generation

Test data generation focuses on automatically creating diverse and representative datasets for evaluating software and machine learning systems, aiming to improve testing efficiency and effectiveness. Current research emphasizes using large language models and generative adversarial networks to synthesize test data, including programs that generate test data, and adapting existing standards like ATML for machine learning applications. This field is crucial for ensuring the reliability and safety of complex systems, particularly in safety-critical domains like avionics and autonomous vehicles, by enabling more comprehensive and automated testing.