Track: Quality Aspects in Machine Learning, AI and Data Analytics


Recent advances in artificial intelligence (AI), especially in machine learning (ML), deep learning (DL) and the underlying data engineering techniques, as well as their integration into software-based systems of all domains raises new challenges to engineering modern AI-based systems. This makes the investigation of quality aspects in machine learning, AI and data analytics an essential topic. AI-based systems are data-intensive, continuously evolving, and self-adapting, which leads to new constructive and analytical quality assurance approaches to guarantee their quality during development and operation in live environments. On the constructive side, for instance, new process models, requirements engineering approaches or continuous integration and deployment models like MLOps are needed. On the analytical side, for instance, new data, offline and online testing approaches are needed for AI-based systems.


In this track submissions on all topics on analytical and constructive quality aspects in machine learning, AI, and data analytics are welcome. In particular, the following topics are of interest:

  • Requirements engineering for AI-based systems

  • Analytical and constructive quality assurance for AI-based systems

  • System and software architecture of AI-based systems

  • Data management and quality for AI-based systems

  • Data, offline and online testing approaches

  • Runtime monitoring, coverage and trace analysis of data, models and code

  • Development processes and organization for machine learning, AI and data analytics

  • Non-functional quality aspects of AI-based systems

  • Quality models, standards and guidelines for developing AI-based systems

  • Empirical studies on quality aspects in machine learning, AI, and data analytics


Chair: Michael Felderer, University of Innsbruck, Austria

Program Committee:

  • Thomas Bach, SAP AG, Germany

  • Markus Borg, RISE SICS AB, Sweden

  • Matteo Camilli, Free University of Bozen-Bolzano, Italy

  • Eduard Paul Enoiu, Mälardalen University, Sweden

  • Harald Foidl, University of Innsbruck, Austria

  • Jürgen Grossmann, Fraunhofer FOKUS, Germany

  • Steffen Herbold, Clausthal University, Germany

  • Marcos Kalinowski, Pontifical Catholic University of Rio de Janeiro, Brazil

  • Foutse Khomh, Polytechnique Montréal, Canada

  • Leandro Minku, University of Birmingham, UK

  • Niklas Lavesson, Blekinge Institute of Technology, Sweden

  • Valentina Lenarduzzi, University of Oulu, Finland

  • Bruno Lima, University of Porto, Portugal

  • Silverio Martínez-Fernández, Universitat Politècnica de Catalunya-Barcelona Tech, Spain

  • Tommi Mikkonen, University of Helsinki, Finland

  • Barbara Plank, IT University of Copenhagen, Denmark

  • Rudolf Ramler, Software Competence Center Hagenberg, Austria

  • Dragos Truscan, Åbo Akademi University, Finland

  • Stefan Wagner, University of Stuttgart, Germany

Prof. Michael Felderer is a professor at the Department of Computer Science at the University of Innsbruck, Austria and a guest professor at the Department of Software Engineering at the Blekinge Institute of Technology, Sweden. His research interests include software quality and testing, AI and software engineering, AI engineering, and empirical software engineering. He has published more than 150 papers and received 12 best paper awards. Prof. Felderer is recognized by the Journal of Systems and Software (JSS) as one of the twenty most active established Software Engineering researchers world-wide in the period 2013 to 2020.


2021, 2020