SEAA 2022 – AI4DevOps

AI-Enabled Software Development and Operations (AI4DevOps)

Technical Track Scope

Motivation: Software engineering increasingly takes advantage of the ability to collect, process, analyze and visualize large quantities of data originating from the development and operations (DevOps) of software applications, products, and services. We see a rise of AI techniques being employed to leverage this data. Examples include deep learning to facilitate accurate classification and prediction, transfer learning to enable cross-system/-project/-organizational knowledge exchange, and reinforcement learning to provide advanced autonomous planning and thereby empower a new class of self-learning, self-optimizing and self-adapting software systems. AI thus 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. The track is an integral part of the 48th Euromicro Conference on Software Engineering and Advanced Applications (SEAA) 2021.

Mission: This track will look at AI-enabled techniques, methods and tools for software development and operations. In particular, we will explore the opportunities and challenges of using AI for software engineering, serving as a forum for the exchange of ideas, solutions and experiences among researchers and practitioners from communities, such as software analytics, quality assurance and testing, software processes, software visualization, human-computer interaction, adaptive systems, AI, data management, and machine learning.


Topics of interest include, but are not restricted to:

  • Methods, tools, applications and lessons learned of AI for software engineering
  • AI-enabled requirements engineering, planning, coding, building and testing
  • AI-enabled release, deployment, monitoring and dynamic adaptation
  • Data-driven AI (machine learning) and big data analytics for DevOps
  • Self-adaptive, self-optimizing and self-learning software systems
  • Explainable and Compliant AI in the context of DevOps
  • AI-driven continuous testing and experimentation
  • Software and data life-cycle management
  • Integrated software and data engineering
  • New applications for deep learning, advanced analytics and reasoning
  • Predictive methods and estimation in software development, operations, and evolution
  • Software visualization and visual analytics
  • Risk management in software and systems development projects
  • Empirical studies and experience reports about successful or unsuccessful applications of the aforementioned topics

Track Organizers

Michel Chaudron,, University of Gothenburg, Sweden
Michael Felderer,, University of Innsbruck, Austria
Andreas Metzger,, University of Duisburg-Essen & Big Data Value Association (BDVA), Germany
Rudolf Ramler,, Software Competence Center Hagenberg GmbH, Austria
Dietmar Winkler,, TU Vienna, Austria

Program Committee

  • Ayse Bener, Ryerson University, Canada
  • Matteo Camilli , Free University of Bolzano, Italy
  • Luis Cruiz, University of Delft, The Netherlands
  • Maya Daneva, Univeristy of Twente, The Netherlands
  • Oscar Dieste, Universidad Politecnica de Madrid, Spain
  • Steffen Frey, University of Stuttgart, Germany
  • Matthias Galster, University of Canterbury, New Zealand
  • Jens Heidrich, Fraunhofer IESE, Germany
  • Michael Kläs, Fraunhofer IESE, Germany
  • Bin Lin, University of Lugano, Switzerland
  • Nazim Madhavji, University of Western Ontario, Canada
  • Andriy Miranskyy , Ryerson University, Canada
  • Zoltan Mann, University of Duisburg-Essen, Germany
  • Sandro Morasca, University of Insubria, Italy
  • Raimund Moser, Free University of Bolzano, Italy
  • Ezequiel Scott, University of Tartu, Estonia
  • Stefan Wagner, University of Stuttgart, Germany