software testing

The automation of software test case generation from natural language requirements remains a critical challenge in software engineering.  While large language models (LLMs) demonstrate impressive generation capabilities, they suffer from high discrepancy rates (up to 57% for direct generation), hallucinated test steps, and lack formal verification mechanisms for safety-critical constraints.  This paper presents a novel algorithmic framework that addresses these limitations through five principal contributions.  First, we introduce the Neuro-Symbolic Requirements Graph (

The paper is devoted to the development of a prototype information system based on ontological modeling using logical inference (descriptive logic) in the process of software testing.

The actual problem of using situational awareness as a key factor in designing the decision support system is considered.

For the practical application of the developed methods of critical situations identification the software testing industry has been selected. It is related to the complexity of the software development processes and the high cost of error.