Data and AI Driven Engineering (DAIDE)

 

Motivation

Over the last decade, artificial intelligence (AI) and specifically machine- and deep-learning (ML/DL) solutions have become critical for software engineering. 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 analytics, for A/B testing, as well as for training ML/DL models. In parallel, the progress in high-performance parallel hardware, such as GPUs, TPUs 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 data-driven practices and ML/DL solutions in industry.

In industry, the hype around AI has resulted in virtually every company having some form of AI initiative, or host of AI initiatives, and the number of experiments and prototypes 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.

One of the important application domains for AI is software engineering. AI can be used for, among others, software analytics, quality assurance and testing, software processes, software visualization, human-computer interaction, adaptive systems, and data management. AI enables us to take the next step in excelling in software development and operations by delivering smarter software monitoring, analytics, development and management techniques, methods, and tools. Specifically, DevOps, due to the periodic nature and significant amounts of data generated, is highly suitable for the application of ML/DL models.

This track is concerned with novel research results in engineering to facilitate production-quality AI solutions in all relevant domains, including software engineering.

Topics

Topics of interest include, but are not restricted to:

  • Solutions to assess and guarantee data quality for ML, including data pipelines
  • Design methods and approaches for developing ML/DL models
  • Distributed ML/DL models in embedded systems, including federated learning and distributed AI
  • Methods, tools, applications and lessons learned of AI for software engineering
  • Automated labeling of data for ML
  • Adoption of DevOps, DataOps, and/or MLOps practices in large-scale software engineering
  • AI and analytics for DevOps
  • Engineering aspects of training, transfer learning and reinforcement learning
  • Engineering effective ML/DL deployments
  • Automated experimentation and autonomously improving systems
  • Feature experimentation and data driven development practices (e.g., A/B testing)
  • Reinforcement learning and multi-armed bandits
  • Explainable and compliant AI in the context of DevOps
  • Predictive methods and estimation in software development, operations, and evolution
  • Software visualization and visual analytics
  • System and software requirements and their relationship to AI/ML modeling
  • System and software architectures for AI-enabled systems
  • Software development life cycle for data and AI-enabled systems
  • Managing non-functional properties of data and AI-enabled systems
  • Empirical studies and experience reports about successful or unsuccessful applications of the aforementioned topics

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, helena.holmstrom.olsson@mau.se, Malmö University, Sweden

Jan Bosch, jan.bosch@chalmers.se, Chalmers University of Technology, Sweden

Program Committee

  • Michel Chaudron, Technician University Eindhoven, The Netherlands
  • Maya Daneva, University of Twente, The Netherlands
  • Philipp Haindl, St. Pölten University of Applied Sciences, Austria
  • Aleksander Fabijan, Microsoft, USA
  • Ilias Gerostathopoulos, Vrije Universiteit Amsterdam, The Netherlands
  • Jens Heidrich, Fraunhofer IESE, Germany
  • Hans-Martin Heyn, University of Gothenburg, Sweden
  • Nazim Madhavji, University of Western Ontario, Canada
  • Zoltan Mann, University of Duisburg-Essen, Germany
  • Henry Muccini, L’Aquila University, Italy
  • Rudolf Ramler, Software Competence Center Hagenberg GmbH, Austria
  • Daniela Soares Cruzes, Norwegian University of Science and Technology
  • Karthik Vaidhyanathan, Software Engineering Research Center, IIIT-Hyderabad, India
  • Dietmar Winkler, Center for Digital Production (CDP) & TU Wien, Vienna, Austria
  • Petra Heck, Fontys Kenniscentrum Applied AI for Society, TU/e

  • Christoph Elsner, Siemens AG, Germany

  • Matthias Tichy, Ulm University, Germany

  • Matteo Camilli, Free University of Bolzano, Italy

  • Michael Kläs, Fraunhofer IESE, Germany

  • Luis Cruiz, University of Delft, The Netherlands

  • Ipek Ozkaya, Software Engineering Institute, Carnegie Mellon University, USA

  • Steffen Frey, University of Groningen, The Netherlands

  • Stefan Wagner, Technical University Munich, Germany
  • Diana-Laura Boris, Babes-Bolyai University, Romania