FORMALIZATION OF AUTOMATED TESTING PROCESSES OF COMPLEX SOFTWARE SYSTEMS BASED ON MULTIAGENT MODELING

Abstract

The study is devoted to the formalization of automated testing processes for complex software systems operating in dynamic and stochastic environments. It is shown that traditional testing methods based on fixed test scenarios do not ensure sufficient adaptability and scalability as the complexity of modern software increases. The research substantiates the feasibility of applying a multi-agent approach as an effective tool for building intelligent testing systems capable of self-organization, learning, and optimization of defect detection processes. The study performs a system-theoretic analysis of the testing process as a dynamic system composed of multiple autonomous agents interacting with each other and with the testing environment. A generalized formal model of the multi-agent testing process is proposed, which defines the main structural elements: a set of agents, the environment of their interaction, the test scenario space, behavioral policies, and the evaluation function of the obtained results. This approach allows considering testing as an evolutionary process in which agents adapt their strategies based on accumulated experience and feedback, gradually improving the efficiency and completeness of software verification. A graph-based and stochastic model of the test scenario space is developed, taking into account the probability of defect detection, testing cost and duration, coverage degree, and other efficiency attributes. A system of metrics is defined, encompassing both classical measures of testing performance and multi-agent specific parameters such as cooperativity, conflict rate, and convergence speed of agent policies. Algorithms for model correctness verification are described, providing consistency, reach- ability, stability, and adequacy assessment under stochastic disturbances. The obtained results form a theoretical foundation for developing intelligent multi-agent systems of automated testing capable of adaptation and self-organization. The proposed approach enhances the efficiency of the testing process, reduces time and resource costs, and increases software reliability. Future research will focus on developing agent learning mechanisms and integrating the proposed model into CI/CD processes and continuous quality control systems.