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Quality Assurance for AI-Based Systems: Overview and Challenges (Introduction to Interactive Session)

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Software Quality: Future Perspectives on Software Engineering Quality (SWQD 2021)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 404))

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Abstract

The number and importance of AI-based systems in all domains is growing. With the pervasive use and the dependence on AI-based systems, the quality of these systems becomes essential for their practical usage. However, quality assurance for AI-based systems is an emerging area that has not been well explored and requires collaboration between the SE and AI research communities. This paper discusses terminology and challenges on quality assurance for AI-based systems to set a baseline for that purpose. Therefore, we define basic concepts and characterize AI-based systems along the three dimensions of artifact type, process, and quality characteristics. Furthermore, we elaborate on the key challenges of (1) understandability and interpretability of AI models, (2) lack of specifications and defined requirements, (3) need for validation data and test input generation, (4) defining expected outcomes as test oracles, (5) accuracy and correctness measures, (6) non-functional properties of AI-based systems, (7) self-adaptive and self-learning characteristics, and (8) dynamic and frequently changing environments.

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Acknowledgements

The research reported in this paper has been partly funded by the Federal Ministry for Climate Action, Environment, Energy, Mobility, Innovation and Technology (BMK), the Federal Ministry for Digital and Economic Affairs (BMDW), and the Province of Upper Austria in the frame of the COMET - Competence Centers for Excellent Technologies Programme managed by Austrian Research Promotion Agency FFG.

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Authors and Affiliations

  1. University of Innsbruck, Innsbruck, Austria

    Michael Felderer

  2. Software Competence Center Hagenberg GmbH (SCCH), Hagenberg im Mühlkreis, Austria

    Rudolf Ramler

Authors
  1. Michael Felderer
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  2. Rudolf Ramler
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Corresponding author

Correspondence to Michael Felderer .

Editor information

Editors and Affiliations

  1. TU Wien, Vienna, Austria

    Dietmar Winkler

  2. TU Wien, Vienna, Austria

    Stefan Biffl

  3. Blekinge Institute of Technology, Karlskrona, Sweden

    Daniel Mendez

  4. Johannes Kepler University Linz, Linz, Austria

    Manuel Wimmer

  5. Software Quality Lab GmbH, Linz, Austria

    Johannes Bergsmann

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Felderer, M., Ramler, R. (2021). Quality Assurance for AI-Based Systems: Overview and Challenges (Introduction to Interactive Session). In: Winkler, D., Biffl, S., Mendez, D., Wimmer, M., Bergsmann, J. (eds) Software Quality: Future Perspectives on Software Engineering Quality. SWQD 2021. Lecture Notes in Business Information Processing, vol 404. Springer, Cham. https://doi.org/10.1007/978-3-030-65854-0_3

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  • DOI: https://doi.org/10.1007/978-3-030-65854-0_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-65853-3

  • Online ISBN: 978-3-030-65854-0

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