Adaptive Synthesis of Intelligent Monitoring Models for LLM-modified Information Systems: A Genetic Optimisation Approach

Vasyl Lyashkevych

doi:10.2478/acss-2026-0010

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Adaptive Synthesis of Intelligent Monitoring Models for LLM-modified Information Systems: A Genetic Optimisation Approach

Applied Computer Systems

Volume 31 (2026): Issue 1 (January 2026)

By: Vasyl Lyashkevych

Open Access

|Jun 2026

Abstract

This study investigates adaptive synthesis of intelligent monitoring (IM) for applications modified by large language models (LLMs). A multi-objective, property-driven genetic approach with a tensor of chromosome representation is proposed to generate monitoring configurations under architectural changes. Using migration from Oracle to PostgreSQL as a controlled case, the study shows that functional equivalence can be maintained while monitoring-related properties drift, supporting adaptive archi tecture-aware monitoring over static settings.

References

A. Fan, Gokkaya, M. Harman, M. Lyubarskiy, S. Sengupta, S. Yoo, and J. M. Zhang, “Large language models for software engineering: Survey and open problems,” in 2023 IEEE/ACM International Conference on Software Engineering: Future of Software Engineering (ICSE-FoSE), Melbourne, Australia, May 2024, pp. 31–53. https://doi.org/10.1109/ICSE-FoSE59343.2023.00008
Search in Google Scholar Back to article
V. Terragni, A. Vella, P. Roop, and K. Blincoe, “The future of AI-driven software engineering,” ACM Transactions on Software Engineering and Methodology, vol. 34, no. 5, May 2025, Art. no. 120. https://doi.org/10.1145/3715003
Search in Google Scholar Back to article
X. Hou et al., “Large language models for software engineering: A systematic literature review,” ACM Transactions on Software Engineering and Methodology, vol. 33, no. 8, Dec. 2024, Art. no. 220. https://doi.org/10.1145/3695988
Search in Google Scholar Back to article
M. Schäfer, S. Nadi, A. Eghbali, and F. Tip, “An empirical evaluation of using large language models for automated unit test generation,” IEEE Transactions on Software Engineering, vol. 51, no. 1, pp. 85–105, Jan. 2024. https://doi.org/10.1109/TSE.2023.3334955
Search in Google Scholar Back to article
L. Yang et al., “On the evaluation of large language models in unit test generation,” in Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering, Oct. 2024, pp. 1607–1619. https://doi.org/10.1145/3691620.3695529
Search in Google Scholar Back to article
Y. Chen, Z. Hu, C. Zhi, J. Han, S. Deng, and J. Yin, “ChatUniTest: A framework for LLM-based test generation,” in Companion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering, July 2024, pp. 572–576. https://doi.org/10.1145/3663529.3663801
Search in Google Scholar Back to article
A. M. Dakhel, A. Nikanjam, V. Majdinasab, F. Khomh, and M. Desmarais, “Effective test generation using pre-trained large language models and mutation testing,” Information and Software Technology, vol. 171, July 2024, Art. No. 107468. https://doi.org/10.1016/j.infsof.2024.107468
Search in Google Scholar Back to article
N. Alshahwan “Automated unit test improvement using large language models at Meta,” in Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering, July 2024, pp. 185–196. https://doi.org/10.1145/3663529.3663839
Search in Google Scholar Back to article
R. de Lemos al., “Software engineering for self-adaptive systems: A second research roadmap,” in Software Engineering for Self-Adaptive Systems II. Lecture Notes in Computer Science, R. de Lemos, H. Giese, H. A. Müller, and M. Shaw, Eds., vol 7475. Springer, Berlin, Heidelberg, 2013, pp. 1–32. https://doi.org/10.1007/978-3-642-35813-5_1
Search in Google Scholar Back to article
V. E. S. Souza, A. Lapouchnian, and J. Mylopoulos, “Awareness requirements for adaptive systems,” in Proceedings of the 6th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, May 2011, pp. 60–69. https://doi.org/10.1145/1988008.1988018
Search in Google Scholar Back to article
N. M. Villegas, G. Tamura, H. A. Müller, L. Duchien, and R. Casallas, “Architecting software systems for runtime self-adaptation: Concepts, models, and challenges,” in Managing Trade-Offs in Adaptable Software Architectures. Elsevier, 2017, pp. 17–43. https://doi.org/10.1016/B978-0-12-802855-1.00002-2
Search in Google Scholar Back to article
E. Henrichs, V. Lesch, M. Strasser, S. Kounev, and C. Krupitzer, “A literature review on optimization techniques for adaptation planning in adaptive systems: State of the art and research directions,” Information and Software Technology, vol. 149, Sep. 2022, Art. no. 106940. https://doi.org/10.1016/j.infsof.2022.106940
Search in Google Scholar Back to article
J. Li, M. Zhang, N. Li, D. Weyns, Z. Jin, and K. Tei, “Generative AI for self-adaptive systems: State of the art and research roadmap,” ACM Transactions on Autonomous and Adaptive Systems, vol. 19, no. 3, Sep. 2024, Art. no. 13. https://doi.org/10.1145/3686803
Search in Google Scholar Back to article
M. Waseem, P. Liang, M. Shahin, A. Di Salle, and G. Márquez, “Design, monitoring, and testing of microservices systems: The practitioners’ perspective,” Journal of Systems and Software, vol. 182, Dec. 2021, Art. no. 111061. https://doi.org/10.1016/j.jss.2021.111061
Search in Google Scholar Back to article
P. Di Francesco, P. Lago, and I. Malavolta, “Architecting with microservices: A systematic mapping study,” Journal of Systems and Software, vol. 150, pp. 77–97, Apr. 2019. https://doi.org/10.1016/j.jss.2019.01.001
Search in Google Scholar Back to article
M. Waseem, P. Liang, and M. Shahin, “A systematic mapping study on microservices architecture in DevOps,” Journal of Systems and Software, vol. 170, Dec. 2020, Art. no. 110798. https://doi.org/10.1016/j.jss.2020.110798
Search in Google Scholar Back to article
O. Grynets, V. Lyashkevych, D. Baran, M. Orliansky, T. Zelenyy, and M. Leshchyshyn, “Fine-tuned LLM-based code migration framework,” arXiv:2512.13515, Dec. 2025. https://arxiv.org/abs/2512.13515
Search in Google Scholar Back to article
A. Aleti, B. Buhnova, L. Grunske, A. Koziolek, and I. Meedeniya, “Software architecture optimization methods: A systematic literature review,” IEEE Transactions on Software Engineering, vol. 39, no. 5, pp. 658–683, May 2013. https://doi.org/10.1109/TSE.2012.64
Search in Google Scholar Back to article
Q. Li, M. Lu, T. Gu, and Y. Wu, “Runtime software architecture-based reliability prediction for self-adaptive systems,” Symmetry, vol. 14, no. 3, Mar. 2022, Art. no. 589. https://doi.org/10.3390/sym14030589
Search in Google Scholar Back to article
V. Y. Lyashkevych, “Sustainable optimization of consolidated data processing algorithms based on machine learning and genetic algorithms,” Electronics and Information Technologies, vol. 32, pp. 39–54, 2025. https://doi.org/10.30970/eli.32.3
Search in Google Scholar Back to article
M. Y. Lyashkevych, V. Y. Lyashkevych, and R. Y. Shuvar, “Definition and formalization of the software functional state concept throughout the development life cycle,” Electronics and Information Technologies, vol. 32, pp. 151–170, 2025. https://doi.org/10.30970/eli.32.11
Search in Google Scholar Back to article
S. S. da Silva, P, Pelliccione, and A. Bertolino, “Self-adaptive testing in the field,” ACM Transactions on Autonomous and Adaptive Systems, vol. 19, no. 1, Feb. 2024, Art. no. 4. https://doi.org/10.1145/3627163
Search in Google Scholar Back to article
E. M. Fredericks, B. DeVries, and B. H. C. Cheng, “Towards run-time adaptation of test cases for self-adaptive systems in the face of uncertainty,” in Proceedings of the 9th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, June 2014, pp. 17–26. https://doi.org/10.1145/2593929.2593937
Search in Google Scholar Back to article
C. E. da Silva and R. de Lemos, “Dynamic plans for integration testing of self-adaptive software systems,” in Proceedings of the 6th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, May 2011, pp. 148–157. https://doi.org/10.1145/1988008.1988029
Search in Google Scholar Back to article
O. Grynets and V. Lyashkevych, “Unified architecture metamodel of information systems developed by generative AI,” arXiv:2604.00171, Mar. 2026. https://doi.org/10.48550/arXiv.2604.00171
Search in Google Scholar Back to article