Call for Papers

Technical Track @ 51st EUROMICRO SEAA Conference

Data and AI Driven Engineering (DAIDE)

https://dsd-seaa.com

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 full paper (8 pages) and short paper (4 pages) 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.

Vision papers 

Software development and engineering is fundamentally changing with the increasing use of AI technologies. What used to be the typical methods, tools, tasks and roles in an R&D organization are rapidly being complemented or even fully replaced with AI/ML technologies. As obvious examples, LLMs like e.g., ChatGPT are proven effective in tasks ranging from code generation, code analysis, test case generation etc., and already now we see how R&D resources are shifting as companies are exploring and trying to figure out what development of software-intensive systems will look like going forward. 

With the vision paper category, we invite for full papers (8 pages) that address the end-to-end fundamental changes we see happening in the development and engineering of software-intensive systems, and the implications of these in industry. Rather than focusing on individual “puzzle pieces”, the intent of vision papers is to provide a perspective on the entire “puzzle”. Vision papers will be evaluated based on the completeness and convincing nature of the presented perspective.

Track Organizers

Helena Holmström Olsson, helena.holmstrom.olsson@mau.se, Malmö University, Sweden

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

Program Committee

  • Matteo Camilli, Free University of Bolzano, Italy
  • Michel Chaudron, Technician University Eindhoven, The Netherlands
  • Philipp Haindl, St. Pölten University of Applied Sciences, Austria
  • Aleksander Fabijan, Microsoft, USA
  • Michael Felderer, Director, Institute for Software Technology, German Aerospace Center (DLR)
  • Christoph Elsner, Siemens AG, Germany
  • Hans-Martin Heyn University of Gothenburg, Sweden
  • Sami Hyrynsalmi, LUT University, Finland
  • Michael Kläs, Fraunhofer IESE, Germany
  • Nazim Madhavji, University of Western Ontario, Canada
  • Andreas Metzger, University of Duisburg-Essen & Big Data Value Association (BDVA), Germany
  • Henry Muccini, L’Aquila University, Italy
  • Daniela Soares, Cruzes Norwegian University of Science and Technology
  • Matthias Tichy, Ulm University, Germany
  • Xiaofeng Wang, Free University of Bozen-Bolzano, Italy
  • Petra Heck, Fontys Kenniscentrum Applied AI for Society, TU/e
  • Rotoloni Gabriele, University of Insumbria, Italy
  • Vaihab Bajpal, Microsoft, USA
  • Suprya Lal, Yelp, USA
  • Maya Daneva, University of Twente, The Netherlands
  •  Jens Heidrich, Fraunhofer IESE, Germany