EVOLUTION-AWARE SPECIFICATION-DRIVEN SYNTHESIS OF INTELLIGENT MONITORING SYSTEMS BY LARGE LANGUAGE MODELS.
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| Title: | EVOLUTION-AWARE SPECIFICATION-DRIVEN SYNTHESIS OF INTELLIGENT MONITORING SYSTEMS BY LARGE LANGUAGE MODELS. |
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| Alternate Title: | ЕВОЛЮЦІЙНО-СВІДОМИЙ СИНТЕЗ ІНТЕЛЕКТУАЛЬНИХ СИСТЕМ МОНІТОРИНГУ ЗА ДОПОМОГОЮ ВЕЛИКИХ МОВНИХ МОДЕЛЕЙ. |
| Authors: | Lyashkevych, V. Y.1 vasyl.liashkevych@lnu.edu.ua |
| Source: | Informatics & Mathematical Methods in Simulation / Informatika ta Matematičnì Metodi v Modelûvannì. 2026, Vol. 16 Issue 3, p424-432. 9p. |
| Subjects: | Software engineering, Adaptive control systems, Context-aware computing, Language models, Decision making |
| Abstract: | This paper proposes an evolution-aware, specification-driven method for synthesizing intelligent monitoring systems using large language models. This approach views monitoring not as a static addition to observability, but as a specification-driven and regenerable information technology for decision support. Starting from a product specification, the method generates a monitoring specification that formalizes monitored entities, contexts, functional states, metrics, alerts, dashboards, admissible monitoring strategies, and adaptation rules. Based on this specification, the system generates monitoring configuration and monitoring code, while a specialized strategy selection layer selects monitoring and decision strategies according to execution contexts, functional states, uncertainties, and monitoring objectives. As the monitored information system evolves, the feedback and evolution mechanism updates the monitoring specification and triggers the regeneration of monitoring artifacts. The method combines ontology-based generation, strategy-based synthesis, and feedback-based adaptation into a single architecture-oriented pipeline. The experimental resources used in the study are artifacts obtained from a fine-tuned code migration framework and from a unified architecture metamodel of information systems, which were fully or partially constructed with the support of generative artificial intelligence. A validation protocol is proposed that combines specification-driven evaluation, metrics oriented on retrieval augmented generation, and regeneration quality indicators. The experimental evaluation indicates that the complete method suggests improvement in validity, strategy correctness, deployment readiness, and regeneration success compared to weaker baselines that lack a clear monitoring specification, ontological constraints, or strategy selection. The proposed approach extends specification-driven software engineering toward the co-evolution of intelligent monitoring systems. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
| Abstract: | This paper proposes an evolution-aware, specification-driven method for synthesizing intelligent monitoring systems using large language models. This approach views monitoring not as a static addition to observability, but as a specification-driven and regenerable information technology for decision support. Starting from a product specification, the method generates a monitoring specification that formalizes monitored entities, contexts, functional states, metrics, alerts, dashboards, admissible monitoring strategies, and adaptation rules. Based on this specification, the system generates monitoring configuration and monitoring code, while a specialized strategy selection layer selects monitoring and decision strategies according to execution contexts, functional states, uncertainties, and monitoring objectives. As the monitored information system evolves, the feedback and evolution mechanism updates the monitoring specification and triggers the regeneration of monitoring artifacts. The method combines ontology-based generation, strategy-based synthesis, and feedback-based adaptation into a single architecture-oriented pipeline. The experimental resources used in the study are artifacts obtained from a fine-tuned code migration framework and from a unified architecture metamodel of information systems, which were fully or partially constructed with the support of generative artificial intelligence. A validation protocol is proposed that combines specification-driven evaluation, metrics oriented on retrieval augmented generation, and regeneration quality indicators. The experimental evaluation indicates that the complete method suggests improvement in validity, strategy correctness, deployment readiness, and regeneration success compared to weaker baselines that lack a clear monitoring specification, ontological constraints, or strategy selection. The proposed approach extends specification-driven software engineering toward the co-evolution of intelligent monitoring systems. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 22235744 |
| DOI: | 10.15276/imms.v16.no3.424 |
