Paper presents a comprehensive analytical review and synthesis of modern approaches to the development of decision support systems (DSS) based on multi-agent system (MAS) technologies. The evolution of corporate DSS from classical centralized architectures to distributed intelligent systems is examined.
The study aims to justify and test an approach to the automated conceptualization of the DevOps subject area using large language models. This paper explores the potential applications of LLMs in concept classification, ontology construction, and configuration structure explanation. Additionally, it analyzes the models' capabilities in supporting the training and auditing of DevOps processes. The research methodology consists of four stages. First, an initial set of DevOps concepts was generated using an LLM.
The paper is devoted to the research and development of methods and tools for identifying problematic situations on the basis of ontologies using the mechanisms of logical inference that are used in intellectual decision support systems for software testing problems.
The important problem of software testing using ontological modeling for timely detection of errors and improvement of quality of the developed software is considered.