Version 1
: Received: 5 May 2024 / Approved: 6 May 2024 / Online: 7 May 2024 (03:18:35 CEST)
How to cite:
Kaplan, J.; Rabelo, L. Bridging the Gap: Leveraging Informal Software Architecture Artifacts for Structured Model Creation. Preprints2024, 2024050305. https://doi.org/10.20944/preprints202405.0305.v1
Kaplan, J.; Rabelo, L. Bridging the Gap: Leveraging Informal Software Architecture Artifacts for Structured Model Creation. Preprints 2024, 2024050305. https://doi.org/10.20944/preprints202405.0305.v1
Kaplan, J.; Rabelo, L. Bridging the Gap: Leveraging Informal Software Architecture Artifacts for Structured Model Creation. Preprints2024, 2024050305. https://doi.org/10.20944/preprints202405.0305.v1
APA Style
Kaplan, J., & Rabelo, L. (2024). Bridging the Gap: Leveraging Informal Software Architecture Artifacts for Structured Model Creation. Preprints. https://doi.org/10.20944/preprints202405.0305.v1
Chicago/Turabian Style
Kaplan, J. and Luis Rabelo. 2024 "Bridging the Gap: Leveraging Informal Software Architecture Artifacts for Structured Model Creation" Preprints. https://doi.org/10.20944/preprints202405.0305.v1
Abstract
This study addresses the prevalent gap between formal and informal architectural methodologies in software engineering. Recognizing the potential of informal architecture artifacts in analytical processes, we introduce a groundbreaking methodology that efficiently transforms these informal components into structured formal models. This method facilitates a deeper understanding and utilization of informal diagrams and enhances analytical capabilities through graph analysis techniques. By leveraging user-friendly tools like Draw.io, the methodology democratizes the modeling process, making sophisticated architectural analyses accessible to a broader spectrum of professionals without requiring deep expertise in formal methods. The innovative aspects of this methodology lie in its ability to streamline the transformation process, significantly improving both the efficiency and effectiveness of model creation and analysis. These enhancements are demonstrated through a practical application involving a sample architecture diagram, where the resulting model is thoroughly analyzed using advanced graph analysis tools like Python's NetworkX library and Neo4j. This approach bridges the theoretical and practical divides in software architecture and sets a new standard for integrating informal artifacts into systematic engineering workflows. In addition, considerations for Artificial Intelligence developments are discussed.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
