The work explores the digital assessment of the structure of complex software systems, which is an important factor for improving reliability, performance, and scalability. Current design methods lack a formalized and reproducible method for architectural system analysis of components and their interactions, hindering the comparison of alternative architectural solutions and identifying the most effective structural configurations during the design phase. Therefore, this paper focuses on a development method that enables quantitative assessment of the architecture of complex software systems, taking into account the implementation specifics of components and their interactions. The leading approach to studying this problem is a graph representation of the architecture, where the nodes correspond to software components with numerical characteristics according to pre-defined quality criteria, and the edges reflect architectural connections with component influence coefficients. The architectural significance of components is calculated as the average value of coefficients in incoming connections, while components without incoming connections. The final architectural score is defined as a weighted average of local component scores, taking into account their architectural significance, which provides a comprehensive and systematic approach to architectural analysis. The article presents the results of applying the method to a software system with 10 and 13 components, reveals changes in the final assessment when adding new components and changing the connection structure, and identifies the most significant elements of the system from an architectural perspective. The obtained data allows for a quantitative comparison of alternative architectural solutions and identifies the impact of components on the overall system's performance. The article's materials are of practical value for the design, optimization, and modernization of complex software systems, and can also be used in research in software engineering and systems analysis.
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Saenko Iaroslav Dmitrievich
Siberian Federal University
Krasnoyarsk, Russian Federation
