Supporting Process Model Validation through Natural Language Generation

• Published in: IEEE transactions on software engineering Vol. 40; no. 8; pp. 818 - 840
• Main Authors: Leopold, Henrik, Mendling, Jan, Polyvyanyy, Artem
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2014; IEEE Computer Society
• Subjects: Adaptation models; Analytical models; Business; Completeness; Context; Context modeling; Descriptions; Information systems; Linguistics; Mathematical models; Natural language; Natural language processing; Natural languages; Programming languages; Software engineering; Strategy; Studies; Subject specialists; Systems design; Systems development; Texts; Unified modeling language; verbalization; natural language text generation; Business process model validation
• ISSN: 0098-5589; 1939-3520
• DOI: 10.1109/TSE.2014.2327044

The design and development of process-aware information systems is often supported by specifying requirements as business process models. Although this approach is generally accepted as an effective strategy, it remains a fundamental challenge to adequately validate these models given the diverging skill set of domain experts and system analysts. As domain experts often do not feel confident in judging the correctness and completeness of process models that system analysts create, the validation often has to regress to a discourse using natural language. In order to support such a discourse appropriately, so-called verbalization techniques have been defined for different types of conceptual models. However, there is currently no sophisticated technique available that is capable of generating natural-looking text from process models. In this paper, we address this research gap and propose a technique for generating natural language texts from business process models. A comparison with manually created process descriptions demonstrates that the generated texts are superior in terms of completeness, structure, and linguistic complexity. An evaluation with users further demonstrates that the texts are very understandable and effectively allow the reader to infer the process model semantics. Hence, the generated texts represent a useful input for process model validation.