Generation of monitoring functions in production automation using test specifications

Suhyun Cha, Sebastian Ulewicz, Birgit Vogel-Heuser, Alexander Weigl, Mattias Ulbrich, Bernhard Beckert

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

High quality requirements are set for automated production systems (aPS) as malfunctions can harm humans or cause severe financial loss. These malfunctions can be caused by faults in the control software of the aPS or its inability to correctly identify and handle unintended situations and errors in the technical process or hardware behavior. To achieve more dependable control software, software testing and formal verification can be used to find faults in the software, but require to make assumptions about possible situations (inputs) occurring in the aPS during runtime and often only allow the validation of specific cases. Monitoring individual functions within the control software during runtime can help to identify unspecified situations and raise warnings of the uncertainty about the suitability of a reaction. Yet, the design of reliable monitoring functions requires extensive experience and resources. For this reason, we propose a method for generating monitoring functions from available testing and verification specifications initially used for validating a control software function. Through this, it is possible to continuously assess the behavior of individual software functions and to identify and warn about a) violations of the test specification during runtime and b) unintended situations in which correct software behavior was never tested. Thus, the approach can help to assess and improve both the control software and specification quality through observation and behavior assessment far beyond the testing phase by efficiently reusing existing test specifications for runtime monitoring.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE 15th International Conference on Industrial Informatics, INDIN 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages339-344
Number of pages6
ISBN (Electronic)9781538608371
DOIs
StatePublished - 10 Nov 2017
Event15th IEEE International Conference on Industrial Informatics, INDIN 2017 - Emden, Germany
Duration: 24 Jul 201726 Jul 2017

Publication series

NameProceedings - 2017 IEEE 15th International Conference on Industrial Informatics, INDIN 2017

Conference

Conference15th IEEE International Conference on Industrial Informatics, INDIN 2017
Country/TerritoryGermany
CityEmden
Period24/07/1726/07/17

Keywords

  • automatic testing
  • manufacturing automation
  • model-driven development
  • system testing

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