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Measuring Complexity of Bot Models in Robotic Process Automation

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Business Process Management: Blockchain, Robotic Process Automation, Central and Eastern European, Educators and Industry Forum (BPM 2024)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 527))

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Abstract

Robotic process automation (RPA), a technology to automate structured tasks on computers in a light-weight fashion, thrives on graphical models, enabling business users and citizen developers to create automation workflows without programming knowledge. However, due to the created flows often not being optimized and the fine-grained nature of the RPA instructions, these models quickly become complex and extensive, complicating their maintenance and comprehensibility. In this paper, we draw inspiration from the related fields of software programming and business process modeling to introduce complexity metrics for RPA bot models. These allow to objectively measure and compare the complexity of such workflows, and can thus, for example, provide an indication of where to start refactoring models in the bot repository.

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Notes

  1. 1.

    As in related work, we use the term metric to also refer to measures (e.g., [6, 19]).

  2. 2.

    Entering the first context is also considered a context switch, i. e., NOCS \(\ge \) NOC.

  3. 3.

    In contrast to Cardoso et al., we use the original length formula, rather than the formula for the estimated program length [12], since we want to assess exactly the present model.

  4. 4.

    https://github.com/bptlab/onto-rpa-platform/tree/main/components/metrics.

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Authors and Affiliations

  1. Hasso Plattner Institute, University of Potsdam, Potsdam, Germany

    Maximilian Völker & Mathias Weske

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  1. Maximilian Völker
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Correspondence to Maximilian Völker .

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Editors and Affiliations

  1. Utrecht University, Utrecht, The Netherlands

    Claudio Di Ciccio

  2. University of Vienna, Vienna, Austria

    Walid Fdhila

  3. Sapienza University of Rome, Rome, Italy

    Simone Agostinelli

  4. University of Ottawa, Ottawa, ON, Canada

    Daniel Amyot

  5. Kühne Logistics University, Hamburg, Germany

    Henrik Leopold

  6. Masaryk University, Brno, Czech Republic

    Michal Krčál

  7. TU Wien, Vienna, Austria

    Monika Malinova Mandelburger

  8. University of Maribor, Maribor, Slovenia

    Gregor Polančič

  9. University of Zagreb, Varaždin, Croatia

    Katarina Tomičić-Pupek

  10. AGH University of Krakow, Kraków, Poland

    Katarzyna Gdowska

  11. University of St. Gallen, St. Gallen, Switzerland

    Thomas Grisold

  12. University of Gdańsk, Gdańsk, Poland

    Piotr Sliż

  13. Utrecht University, Utrecht, The Netherlands

    Iris Beerepoot

  14. University of Warsaw, Warsaw, Poland

    Renata Gabryelczyk

  15. University of Duisburg-Essen, Essen, Germany

    Ralf Plattfaut

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Völker, M., Weske, M. (2024). Measuring Complexity of Bot Models in Robotic Process Automation. In: Di Ciccio, C., et al. Business Process Management: Blockchain, Robotic Process Automation, Central and Eastern European, Educators and Industry Forum. BPM 2024. Lecture Notes in Business Information Processing, vol 527. Springer, Cham. https://doi.org/10.1007/978-3-031-70445-1_10

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  • DOI: https://doi.org/10.1007/978-3-031-70445-1_10

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