Our research focuses on the interface between software engineering and deep learning with the goal of improving the robustness, reliability, and dependability of data-intensive software systems.
Current projects involve test generation, monitoring techniques, automated functional oracles, and domain transferability for deep learning-based systems, with a particular focus on autonomous vehicles, as well as the robustness and maintainability of test suites of modern web applications.
The organization contains the GitHub repositories with the research carried out at the Automated Software Testing (AST) Field of Competence at fortiss and the Chair of Software Engineering for Data-intensive Applications of the School of Computation, Information and Technology of the Technical University of Munich (TUM).
You will find the algorithms and tools we developed in our research papers.