@INPROCEEDINGS{DosSantos22-IRC-P-AcceptanceTestingRobots,
   AUTHOR       = {dos Santos, Marcela G. and Fabio Petrillo and 
      Sylvain Hall� and Yann-Ga�l Gu�h�neuc},
   BOOKTITLE    = {Proceedings of the 6<sup>th</sup> International Conference on Robotic Computing (IRC)},
   TITLE        = {An Approach to Apply Automated Acceptance Testing for 
      Industrial Robotic Systems},
   YEAR         = {2022},
   OPTADDRESS   = {},
   OPTCROSSREF  = {},
   EDITOR       = {Daniela D'Auria and Peter Kazanzides and Eric Matson and 
      Luca Muratore},
   MONTH        = {December},
   NOTE         = {Poster. 2 pages.},
   OPTNUMBER    = {},
   OPTORGANIZATION = {},
   PAGES        = {N/A},
   PUBLISHER    = {IEEE CS Press},
   OPTSERIES    = {},
   OPTVOLUME    = {},
   KEYWORDS     = {Topic: <b>ESE for the IoT</b>, 
      Topic: <b>Test case generation</b>, Venue: <c>IRC</c>},
   URL          = {http://www.ptidej.net/publications/documents/IRC22.doc.pdf},
   ABSTRACT     = {Industrial robotic systems (IRS) are systems composed of 
      industrial robots that automate industrial processes. They execute 
      repetitive tasks with high accuracy, replacing or supporting 
      dangerous jobs. Consequently, a low failure rate is crucial in IRS. 
      However, to the best of our knowledge, there is a lack of automated 
      software testing for industrial robots. In this paper, we describe a 
      test strategy implementation to apply BDD to automate acceptance 
      testing for IRS.}
}

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