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The relevance of scientific work lies in the need to improve existing software designed to analyze the compliance of the results of software testing of the stated requirements. For the implementation of this goal, neural networks can be used by quality control specialists to m ake decisions about software quality, or project managers as an expert system, for one of the quality indicators for the customer. The article deals with software testing which is a process of validation and verification of compliance of the software application or business program with the technical requirements that guided its design and development, and work as expected, and identifies important errors or deficiencies classified by the severity of the program to be fixed. Existing systems do not provide for or have only partial integration of systems of work with the analysis of requirements, which should ensure the formation of expert assessment and provide an opportunity to justify the quality of the software product. Thus, a data processing model based on a fuzzy neural network was proposed. An approach to allow determining the compliance of the developed software with functional and non-functional requirements was proposed, taking into account how successfully or unsuccessfully implemented this or that requir ement. The ultimate goal of scientific work is the development of algorithmic software analysis of compliance of software testing results to stated requirements for support in the decisions taken. The following tasks are solved in scientific work: analysis of advantages and disadvantages of using existing systems when working with requirements; definition of general structure and classification of testing and requirements; characteristic main features of the use of neural networks; designing architecture, the module of research of conformity of results of testing software to the stated requirements.

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