ARivaT: A Tool for Automated Generation of Riva-Based Business Process Architecture Diagrams

Article


Hassan, O. A-H., Samhan, A., Alhajhassan, S. and Hammad, R. 2024. ARivaT: A Tool for Automated Generation of Riva-Based Business Process Architecture Diagrams. IEEE Access. 11, pp. 46257-46270. https://doi.org/10.1109/ACCESS.2023.3275932
AuthorsHassan, O. A-H., Samhan, A., Alhajhassan, S. and Hammad, R.
Abstract

The Riva approach is used to develop an object-based Business Process Architecture (BPA) that helps in capturing the full organizational strategies. However, due to the lack of effective tools that can generate Riva BPA models from available artifacts, producing such models is time consuming non-automatic process and it requires an exhaustive manual validation to ensure the development of error-free models. This paper introduces a novel and domain-independent tool titled ‘ARivaT’ to automatically generate Riva BPA models from available knowledge assets such as Units of Work diagrams. ARivaT is underpinned by a step-by-step methodological approach that automates the process of generating Units of Work, First-Cut, and Second-Cut Process Architecture Diagrams. It also provides stakeholders with insightful explanations to allow a precise understanding of business processes and workflow. Furthermore, ARivaT employs a rule-based mechanism to seamlessly validate all the generated Riva BPA models. A case study-based approach has been followed to evaluate the applicability of ARivaT to derive Riva BPA diagrams. The results of our experiment are promising since 82 percent time saving has been recorded when using ARivaT to generate Riva BPA diagrams in comparison to using general drawing tools such as MS Word and MS Visio. In addition, 69 percent time saving has been noted when performing the same task with ARivaT as opposed to specialized tools such as Camunda.

KeywordsRiva; business process architecture; business process management; software requirements; automation; forward engineering
JournalIEEE Access
Journal citation11, pp. 46257-46270
ISSN2169-3536
Year2024
PublisherIEEE
Publisher's version
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Anyone
Digital Object Identifier (DOI)https://doi.org/10.1109/ACCESS.2023.3275932
Publication dates
Online12 May 2023
Publication process dates
Deposited04 Jun 2024
Copyright holder© 2023, The Authors
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