@conference {2020:bpmn-sketch-miner:models,
	title = {From Text to Visual BPMN Process Models: Design and Evaluation},
	booktitle = {23rd International Conference on Model Driven Engineering Languages and Systems (MODELS)},
	year = {2020},
	month = {October},
	pages = {229{\textendash}239},
	publisher = {ACM/IEEE},
	organization = {ACM/IEEE},
	address = {Montreal, Canada},
	abstract = {Most existing Business Process Model and Notation (BPMN) editing tools are graphical, and as such based on explicit modeling, requiring good knowledge of the notation and its semantics, as well as the ability to analyze and abstract business requirements and capture them by correctly using the notation. As a consequence, their use can be cumbersome for live modeling during interviews and design workshops, where participants should not only provide input but also give feedback on how it has been represented in a model. To overcome this, in this paper we present the design and evaluation of BPMN Sketch Miner, a tool which combines notes taking in constrained natural language with process mining to automatically produce BPMN diagrams in real time as interview participants describe them with stories. In this work we discuss the design decisions regarding the trade-off between using mining vs. modelling in order to: 1) support a larger number of BPMN constructs in the textual language; 2) target both BPMN beginners and business analysts, in addition to the process participants themselves. The evaluation of the new version of the tool in terms of how it balances the expressiveness and learnability of its DSL with the usability of the text-to-visual sketching environment shows encouraging results. Namely while BPMN beginners could model a non-trivial process with the tool in a relatively short time and with good accuracy, business analysts appreciated the usability of the tool and the expressiveness of the language in terms of supported BPMN constructs.},
	keywords = {BPMN, domain specific languages, live modeling, text to visual},
	doi = {10.1145/3365438.3410990},
	author = {Ana Ivanchikj and Souhaila Serbout and Cesare Pautasso}
}
