Method for the Automated Identification of Bounded Contexts in the Design of E-Commerce Systems

Authors

DOI:

https://doi.org/10.31649/1997-9266-2026-186-3-27-34

Keywords:

Bounded Context, Domain-Driven Design, large language models, semantic clustering, Context Map, e-commerce

Abstract

The article examines the issue of automating the strategic stage of e-commerce system design, focusing on modeling complex business logic and structuring the domain. Modern e-commerce systems are characterized by a high level of complexity in business rules and a large number of interconnected processes. Effective management of this complexity is possible through the application of the Domain-Driven Design approach, which involves decomposing the system into logically separated modules known as Bounded Contexts. The quality of identifying these contexts directly affects the system’s viability, the clarity of the codebase, and its potential for further evolution.

Traditional approaches to identifying context boundaries are based mainly on heuristic methods and expert sessions, which are resource-intensive and dependent on the human factor. Existing formal methods that rely on structural data analysis are often unable to correctly interpret the semantic nuances of business terminology [1]. This paper proposes an information technology approach that uses the capabilities of generative artificial intelligence and large language models (LLMs) for automated analysis of the problem space.

The main focus of the article is the development of a method for semantic clustering of requirements, which makes it possible to identify hidden linguistic patterns in the description of business processes. The proposed approach involves using LLMs to analyze the project’s Ubiquitous Language and to form a Context Map based on the semantic similarity of concepts rather than only their technical relationships. An algorithm is described that transforms textual specifications and user stories into formalized domain models, determining the recommended boundaries of responsibility for each module.

The research results demonstrate that applying a generative approach makes it possible to significantly increase the objectivity of domain modeling, minimize the cognitive load on architects, and ensure validation of the system’s logical integrity at the early stages of design. The developed technology serves as an intelligent decision-support tool, enabling the creation of flexible and business-change-adaptive e-commerce models.

Author Biographies

S. G. Moskovko, Vinnytsia National Technical University

Post-Graduate Student of the Chair of Automation and Intelligent Information Technologies

R. N. Kvetny, Vinnytsia National Technical University

 Dr. Sc. (Eng.), Professor, Professor of the Chair of Automation and Intelligent Information Technologies

References

W. K. G. Assunção, S. R. Vergilio, and R. E. Lopez-Herrejon, “A survey on microservices extraction approaches,” arXiv preprint arXiv:2104.09278, 2021. https://doi.org/10.48550/arXiv.2104.09278 .

N. Dragoni et al., “Microservices: Yesterday, today, and tomorrow,” in Present and Ulterior Software Engineering, M. Mazzara and B. Meyer, Eds. Cham: Springer, 2017, pp. 195-216. https://doi.org/10.1007/978-3-319-67425-4_12 .

H. Vural, M. Koyuncu, and S. Guney, “A systematic literature review on microservices,” 2017, pp. 203-217. https://doi.org/10.1007/978-3-319-62407-5_14 . (Scopus)

O. Özkan, Ö. Babur, and M. van den Brand, “Domain-Driven Design in software development. A systematic literature review on implementation, challenges, and effectiveness,” Journal of Systems and Software, vol. 207, Art. no. 112537, 2025. https://doi.org/10.1016/j.jss.2025.112537 . (Scopus, WoS)

N. Reimers, and I. Gurevych, “Sentence-BERT: Sentence embeddings using Siamese BERT-networks,” in Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing, Hong Kong, 2019, pp. 3982-3992. https://doi.org/10.18653/v1/D19-1410 . (Scopus)

A. Vaswani, N. Shazeer, and N. Parmar, “Attention is all you need,” in Advances in Neural Information Processing Systems, vol. 30, 2017, pp. 5998-6008. https://doi.org/10.48550/arXiv.1706.03762 . (Scopus)

X. Hou, et al., “Large language models for software engineering: A systematic literature review,” ACM Transactions on Software Engineering and Methodology, 2024. https://doi.org/10.1145/3643677 . (Scopus, WoS)

Q. Zhang, et al., “A survey on large language models for software engineering,” arXiv preprint arXiv:2312.15223, 2023. https://doi.org/10.48550/arXiv.2312.15223 .

S. Arulmohan, M.-J. Meurs, and S. Mosser, “Extracting domain models from textual requirements in the era of large language models,” in IEEE/ACM International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C), Västerås, 2023, pp. 1-9. https://doi.org/10.1109/MODELS-C59198.2023.00096 . (Scopus)

A. Petukhova, J. P. Matos-Carvalho, and N. Fachada, “Text clustering with large language model embeddings,” International Journal of Cognitive Computing in Engineering, vol. 6, pp. 1-12, 2025. https://doi.org/10.1016/j.ijcce.2024.11.004 . (Scopus)

Gemini Team, R. Anil, et al., “Gemini: A family of highly capable multimodal models,” arXiv preprint arXiv:2312.11805, 2023. https://doi.org/10.48550/arXiv.2312.11805 .

J. White, et al., “A prompt pattern catalog to enhance large language model performance,” arXiv preprint arXiv:2302.11382, 2023. https://doi.org/10.48550/arXiv.2302.11382 .

L. Hubert, and P. Arabie, “Comparing partitions,” Journal of Classification, vol. 2, no. 1, pp. 193-218, 1985. https://doi.org/10.1007/BF01908075 . (Scopus, WoS)

P. J. Rousseeuw, “Silhouettes: A graphical aid to the interpretation and validation of cluster analysis,” Journal of Computational and Applied Mathematics, vol. 20, pp. 53-65, 1987. https://doi.org/10.1016/0377-0427(87)90125-7 . (Scopus, WoS)

A. Singhal, “Modern information retrieval: A brief overview,” IEEE Data Engineering Bulletin, vol. 24, no. 4, pp. 35-43, 2001. [Electronic resource]. Available: https://www.researchgate.net/publication/292766142_Modern_Information_Retrieval_A_Brief_Overview .

S. Fortunato, “Community detection in graphs,” Physics Reports, vol. 486, no. 3-5, pp. 75-174, 2010. https://doi.org/10.1016/j.physrep.2009.11.002 . (Scopus, WoS)

D. Müllner, “Modern hierarchical, agglomerative clustering algorithms,” arXiv preprint arXiv:1109.2378, 2011. https://doi.org/10.48550/arXiv.1109.2378 .

S. Moskovko, and R. Kvyetnyy, “The method of structured transformation of a business process activity diagram into a context map of domain-driven design,” Bulletin of Cherkasy State Technological University, vol. 30, no. 2, pp. 33-43, 2025. https://doi.org/10.62660/bcstu/2.2025.33 .

Downloads

Abstract views: 11

Published

2026-07-06

How to Cite

[1]
S. G. Moskovko and R. N. Kvetny, “Method for the Automated Identification of Bounded Contexts in the Design of E-Commerce Systems”, Вісник ВПІ, no. 3, pp. 27–34, Jul. 2026.

Issue

No. 3 (2026)

Section

COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE AND SYSTEMS ANALYSIS

Metrics

Downloads

Download data is not yet available.

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors who publish with this journal agree to the following terms:

  • Authors retain copyright and grant the journal right of first publication.
  • Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
  • Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).