@article {2022:sosym:bpmn,
	title = {Live process modeling with the BPMN Sketch Miner},
	journal = {Software and Systems Modeling},
	volume = {21},
	year = {2022},
	month = {October},
	pages = {1877{\textendash}1906},
	abstract = {BPMN Sketch Miner is a modeling environment for generating visual business process models starting from constrained natural language textual input. Its purpose is to support business process modelers who need to rapidly sketch visual BPMN models during interviews and design workshops, where participants should not only provide input but also give feedback on whether the sketched visual model represents accurately what has been described during the discussion. In this article, we present a detailed description of the BPMN Sketch Miner design decisions and list the different control flow patterns supported by the current version of its textual DSL. We also summarize the user study and survey results originally published in MODELS 2020 concerning the tool usability and learnability and present a new performance evaluation regarding the visual model generation pipeline under actual usage conditions. The goal is to determine whether it can support a rapid model editing cycle, with live synchronization between the textual description and the visual model. This study is based on a benchmark including a large number of models (1350 models) exported by users of the tool during the year 2020. The main results indicate that the performance is sufficient for a smooth live modeling user experience and that the end-to-end execution time of the text-to-model-to-visual pipeline grows linearly with the model size, up to the largest models (with 195 lines of textual description) found in the benchmark workload.},
	keywords = {BPMN, text to visual, Textual Modelling DSL},
	issn = {1619-1366},
	doi = {10.1007/s10270-022-01009-w},
	url = {https://link.springer.com/article/10.1007/s10270-022-01009-w$\#$citeas},
	author = {Ana Ivanchikj and Souhaila Serbout and Cesare Pautasso}
}
@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}
}
