AIDOaRT - AI-augmented automation for efficient DevOps, a model-based framework for continuous development At RunTime in CPSs

Project: Research project

Project Details


The overall idea of AIDOaRT is to efficiently support requirements, monitoring, modelling, coding, and testing activities during the software development process. AIDOaRT can be used as a platform to extend existing tools. To this intent, the project proposes the use of Model-Driven Engineering (MDE) principles and techniques to provide a model-based framework offering proper methods and related tooling. The projects’ framework will enable the observation and analysis of collected data from both runtime and design time to provide dedicated AI-augmented solutions that will then be validated in concrete industrial cases involving complex CPSs.
Effective start/end date1/04/2131/03/24


Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.
  • Stateful Black-Box Fuzzing of Bluetooth Devices Using Automata Learning

    Pferscher, A. & Aichernig, B., 20 May 2022, NASA Formal Methods: 14th International Symposium, NFM 2022, Pasadena, CA, USA, May 24–27, 2022, Proceedings. Deshmukh, J. V., Havelund, K. & Perez, I. (eds.). Cham: Springer, p. 373-392 20 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 13260 LNCS).

    Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review