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

Project: Research project

Project Details

Description

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.
StatusActive
Effective start/end date1/04/2131/03/24

Fingerprint

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