Data and AI driven engineering (DAIDE)

Special Session Scope

Motivation: Over the last decade, the prominence of artificial intelligence (AI) and specifically machine- and deep-learning (ML/DL) solutions has grown exponentially. Because of the big data era, and with companies collecting customer and product data from an increasing number of connected devices, more data is available than ever before and can be used for training ML/DL solutions. In parallel, the progress in high-performance parallel hardware such as GPUs and FPGAs allows for training solutions of scales unfathomable even a decade ago. These two concurrent technology developments are at the heart of the rapid adoption of ML/DL solutions in industry.

The hype around AI has resulted in virtually every company has some form of AI initiative, or host of AI initiatives, ongoing and the number of experiments and prototypes in industry is phenomenal. However, research shows that the transition from prototype to industry-strength, production-quality deployment of ML/DL models proves to be challenging for many companies. The engineering challenges, and the related data management challenges, prove to be significant even if many data scientists and companies fail to recognize these.

The special session is part of the 48th Euromicro Conference on Software Engineering and Advanced Applications (SEAA) 2022.


Topics of interest include, but are not restricted to:

  • Solutions to assess and guarantee data quality for ML
  • Design methods and approached for ML/DL models
  • Distributed ML/DL models in embedded systems
  • Automated labelling of data for ML
  • Adoption of DataOps, DataOps and/or MLOps practices in large-scale software engineering
  • Engineering aspects of training, transfer learning and reinforcement learning
  • Engineering effective ML/DL deployments
  • Management of data pipelines for ML/DL
  • Automated experimentation and Autonomously improving systems
  • Feature experimentation and data driven development practices (e.g., A/B testing)
  • Federated learning and Distributed AI
  • Reinforcement learning and Multi-armed bandits

In particular, we encourage submissions demonstrating the benefits and/or challenges with regards to the development, deployment and evolution of the technologies mentioned above – as well as the adoption and application of the practices, tools and techniques related to these. We welcome submissions providing empirical case study data to illustrate how companies approach this shift in development paradigms.

Track Organizers

Helena Holmström Olsson,, Malmö University, Sweden
Jan Bosch,, Chalmers University of Technology, Sweden

Program Committee

  • Tomas Bures, Charles University, Czech Republic
  • Pin Chen, Defence Science & Technology, Department of Defence, Australia
  • Philipp Haindl, Software Competence Center Hagenberg, Austria
  • Aleksander Fabijan, Microsoft, USA
  • Michael Felderer, University of Innsbruck, Austria
  • Christoph Elsner, Siemens AG, Germany
  • Ilias Gerostathopoulos, Vrije Universiteit Amsterdam, The Netherlands
  • Hans-Martin Heyn, University of Gothenburg, Sweden
  • Sami Hyrynsalmi, LUT University, Finland
  • Antonio Martini, University of Oslo, Norway
  • Tommi Mikkonen, University of Helsinki, Finland
  • Daniela Soares Cruzes, Norwegian University of Science and Technology
  • Matthias Tichy, Ulm University, Germany
  • Xiaofeng Wang, Free University of Bozen-Bolzano, Italy
  • Stefan Wagner, University of Stuttgart, Germany