As software systems continue to grow in complexity, ensuring their reliability and quality has become increasingly challenging. Modern development practices involve frequent updates, distributed architectures, and rapid deployment cycles, which place significant pressure on traditional testing methods. Conventional approaches, which depend on manual testing and static automation scripts, often fail to keep pace with dynamic requirements, resulting in incomplete test coverage, delayed defect detection, and increased development overhead. This project introduces an Agent Mesh-based intelligent testing framework designed to improve the efficiency and effectiveness of software testing. The proposed system is built on a multi-agent architecture, where each agent is assigned a specific role such as planning test strategies, generating test cases, executing tests, detecting anomalies, and validating outcomes. These agents work collaboratively, enabling the system to handle complex testing workflows in a coordinated and adaptive manner. The framework analyzes application inputs, system behavior, and code changes to automatically generate relevant test scenarios and execute them in real time. It further evaluates the results, identifies potential issues, and prioritizes them based on severity. By incorporating a feedback mechanism, the system continuously refines its testing process, making it more robust and adaptive over time. By reducing dependence on manual intervention and enabling intelligent automation, the proposed approach enhances test coverage, accelerates the testing cycle, and improves overall software quality. The system is scalable and can be extended to integrate with real-time development pipelines, making it suitable for modern agile and continuous integration environments.
S et al. (Mon,) studied this question.