Journal
SOFTWARE AND SYSTEMS MODELING
Volume 21, Issue 5, Pages 1877-1906Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s10270-022-01009-w
Keywords
Business Process Model and Notation (BPMN); Process mining; Domain-specific languages; Performance evaluation
Categories
Funding
- Universita della Svizzera italiana
Ask authors/readers for more resources
BPMN Sketch Miner is a modeling environment that generates visual business process models from constrained natural language input, supporting rapid model editing and real-time synchronization. User studies and surveys confirm that it performs well in meeting user requirements, with the execution time of the text-to-visual pipeline growing linearly with the model size.
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available