Detection of naming convention violations in process models for different languages

• Published in: Decision Support Systems Vol. 56; pp. 310 - 325
• Main Authors: Leopold, Henrik, Eid-Sabbagh, Rami-Habib, Mendling, Jan, Azevedo, Leonardo Guerreiro, Baião, Fernanda Araujo
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.12.2013; Elsevier; Elsevier Sequoia S.A
• Subjects: Applied sciences; Artificial intelligence; Automation; Business; Business process reengineering; Computer science; control theory; systems; Exact sciences and technology; Firm modelling; Information systems. Data bases; Linguistics; Mathematical problems; Memory organisation. Data processing; Naming convention checking; Natural language processing; Operational research and scientific management; Operational research. Management science; Process model quality; Process modeling guidelines; Quality control; Reliability theory. Replacement problems; Software; Speech and sound recognition and synthesis. Linguistics; Studies; Violations; Process modeling guidelines; Natural language processing; Naming convention checking; Process model quality; Multilingualism; Lexicon; Text; Naming; Conceptual analysis; Integrated management; Modeling; Business process; Quality assurance; Business model; Language processing; Linguistics; Firm management; Natural language; Firm
• ISSN: 0167-9236; 1873-5797
• DOI: 10.1016/j.dss.2013.06.014

Companies increasingly use business process modeling for documenting and redesigning their operations. However, due to the size of such modeling initiatives, they often struggle with the quality assurance of their model collections. While many model properties can already be checked automatically, there is a notable gap of techniques for checking linguistic aspects such as naming conventions of process model elements. In this paper, we address this problem by introducing an automatic technique for detecting violations of naming conventions. This technique is based on text corpora and independent of linguistic resources such as WordNet. Therefore, it can be easily adapted to the broad set of languages for which corpora exist. We demonstrate the applicability of the technique by analyzing nine process model collections from practice, including over 27,000 labels and covering three different languages. The results of the evaluation show that our technique yields stable results and can reliably deal with ambiguous cases. In this way, this paper provides an important contribution to the field of automated quality assurance of conceptual models. •We present an automatic technique for detecting violations of naming conventions.•The technique is based on text corpora and independent of linguistic resources.•Because of its design, the approach can be easily adapted to other languages.•The evaluation includes 27,000 labels and three different languages.