Prof. Dr. Andrea Stocco is a Professor at the Technische Universität München (TUM), affiliated with the TUM School of Computation, Information and Technology. His research focuses on the intersection of software engineering and deep learning, particularly addressing the robustness and reliability of data-intensive systems. Key areas include autonomous vehicles, web application testing, and automated functional oracles for deep learning systems. He leads initiatives such as the Lehrstuhl für Software und Systems Engineering , collaborating on projects like CrESt and SUPPRA – Algorand Center of Excellence . Research interests encompass monitoring techniques for AI-driven systems, test suite maintainability, and scenario-based testing of cyber-physical systems (CPS). His work emphasizes practical applications, such as improving testing frameworks for evolving web applications and enhancing interoperability in autonomous driving systems (ADS). Recent efforts include leveraging large language models (LLMs) for secure code assessment and benchmarking generative AI for test input generation. No scientific awards are explicitly mentioned in the provided texts. His publications reflect a strong focus on testing methodologies, with over 40 articles since 2013, covering domains like web test automation, dependency-aware testing, and safety-critical failure prediction in autonomous systems. Advising and grants details are not detailed in the current data, but his lab contributes to TUM's broader efforts in software engineering and systems reliability.
