EVOLUTION-AWARE SPECIFICATION-DRIVEN SYNTHESIS OF INTELLIGENT MONITORING SYSTEMS BY LARGE LANGUAGE MODELS.
Saved in:
| Title: | EVOLUTION-AWARE SPECIFICATION-DRIVEN SYNTHESIS OF INTELLIGENT MONITORING SYSTEMS BY LARGE LANGUAGE MODELS. |
|---|---|
| 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] |
| Copyright of Informatics & Mathematical Methods in Simulation / Informatika ta Matematičnì Metodi v Modelûvannì is the property of Odessa Polytechnic University and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 194922480 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: EVOLUTION-AWARE SPECIFICATION-DRIVEN SYNTHESIS OF INTELLIGENT MONITORING SYSTEMS BY LARGE LANGUAGE MODELS. – Name: TitleAlt Label: Alternate Title Group: TiAlt Data: ЕВОЛЮЦІЙНО-СВІДОМИЙ СИНТЕЗ ІНТЕЛЕКТУАЛЬНИХ СИСТЕМ МОНІТОРИНГУ ЗА ДОПОМОГОЮ ВЕЛИКИХ МОВНИХ МОДЕЛЕЙ. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Lyashkevych%2C+V%2E+Y%2E%22">Lyashkevych, V. Y.</searchLink><relatesTo>1</relatesTo><i> vasyl.liashkevych@lnu.edu.ua</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Informatics+%26+Mathematical+Methods+in+Simulation+%2F+Informatika+ta+Matematičnì+Metodi+v+Modelûvannì%22">Informatics & Mathematical Methods in Simulation / Informatika ta Matematičnì Metodi v Modelûvannì</searchLink>. 2026, Vol. 16 Issue 3, p424-432. 9p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Software+engineering%22">Software engineering</searchLink><br /><searchLink fieldCode="DE" term="%22Adaptive+control+systems%22">Adaptive control systems</searchLink><br /><searchLink fieldCode="DE" term="%22Context-aware+computing%22">Context-aware computing</searchLink><br /><searchLink fieldCode="DE" term="%22Language+models%22">Language models</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+making%22">Decision making</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Informatics & Mathematical Methods in Simulation / Informatika ta Matematičnì Metodi v Modelûvannì is the property of Odessa Polytechnic University and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=194922480 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.15276/imms.v16.no3.424 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 9 StartPage: 424 Subjects: – SubjectFull: Software engineering Type: general – SubjectFull: Adaptive control systems Type: general – SubjectFull: Context-aware computing Type: general – SubjectFull: Language models Type: general – SubjectFull: Decision making Type: general Titles: – TitleFull: EVOLUTION-AWARE SPECIFICATION-DRIVEN SYNTHESIS OF INTELLIGENT MONITORING SYSTEMS BY LARGE LANGUAGE MODELS. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Lyashkevych, V. Y. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: 2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 22235744 Numbering: – Type: volume Value: 16 – Type: issue Value: 3 Titles: – TitleFull: Informatics & Mathematical Methods in Simulation / Informatika ta Matematičnì Metodi v Modelûvannì Type: main |
| ResultId | 1 |
