@article{LeopoldMendlingGuenther2016,
  author    = {Leopold, Henrik and Mendling, Jan and Guenther, Oliver},
  title     = {Learning from Quality Issues of BPMN Models from Industry},
  series = {IEEE software},
  volume    = {33},
  journal   = {IEEE software},
  publisher = {Inst. of Electr. and Electronics Engineers},
  address   = {Los Alamitos},
  issn      = {0740-7459},
  doi       = {10.1109/MS.2015.81},
  pages     = {26 -- 33},
  year      = {2016},
  abstract  = {Many organizations use business process models to document business operations and formalize business requirements in software-engineering projects. The Business Process Model and Notation (BPMN), a specification by the Object Management Group, has evolved into the leading standard for process modeling. One challenge is BPMN's complexity: it offers a huge variety of elements and often several representational choices for the same semantics. This raises the question of how well modelers can deal with these choices. Empirical insights into BPMN use from the practitioners' perspective are still missing. To close this gap, researchers analyzed 585 BPMN 2.0 process models from six companies. They found that split and join representations, message flow, the lack of proper model decomposition, and labeling related to quality issues. They give five specific recommendations on how to avoid these issues.},
  language  = {en}
}
@article{GuentherLeopoldMendling2016,
  author    = {G{\"u}nther, Oliver and Leopold, Henrik and Mendling, Jan},
  title     = {Learning from quality issues of BPMN models from industry},
  series = {CEUR Workshop Proceedings},
  volume    = {1701},
  journal   = {CEUR Workshop Proceedings},
  issn      = {1613-0073},
  pages     = {36 -- 38},
  year      = {2016},
  abstract  = {Many organizations use business process models for documenting their business operations. In recent years, the Business Process Model and Notation (BPMN) evolved into the leading standard for process modeling. However, BPMN is complex: The specification offers a huge variety of different elements and often several representational choices for the same semantics. This raises the question of how well modelers can deal with these choices. Empirical insights into BPMN usage from the perspective of practitioners are still missing. We close this gap by analyzing a large set of BPMN 2.0 process models from practice. We found that particularly representational choices for splits and joins, the correct use of message flow, the proper decomposition of models, and the consistent labeling appear to be connected with quality issues. Based on our findings we give five recommendations how these issues can be avoided in the future. The work summarized in this extended abstract has been published in [LMG16].},
  language  = {en}
}
@article{GuentherLeopoldMendling2016,
  author    = {G{\"u}nther, Oliver and Leopold, Henrik and Mendling, Jan},
  title     = {Learning from quality issues of BPMN models from industry},
  series = {IEEE Software},
  volume    = {33},
  journal   = {IEEE Software},
  number    = {4},
  publisher = {Inst. of Electr. and Electronics Engineers},
  address   = {Los Alamitos},
  issn      = {0740-7459},
  doi       = {10.1109/MS.2015.81},
  pages     = {26 -- 33},
  year      = {2016},
  abstract  = {Many organizations use business process models to document business operations and formalize business requirements in software-engineering projects. The Business Process Model and Notation (BPMN), a specification by the Object Management Group, has evolved into the leading standard for process modeling. One challenge is BPMN's complexity: it offers a huge variety of elements and often several representational choices for the same semantics. This raises the question of how well modelers can deal with these choices. Empirical insights into BPMN use from the practitioners' perspective are still missing. To close this gap, researchers analyzed 585 BPMN 2.0 process models from six companies. They found that split and join representations, message flow, the lack of proper model decomposition, and labeling related to quality issues. They give five specific recommendations on how to avoid these issues.},
  language  = {en}
}