AI is reshaping the software landscape, complicating automated testing, particularly with large language models (LLMs) that yield non-deterministic outputs. Traditional testing methods struggle, leading to increased costs when repeatedly verifying LLM-driven features. To address these challenges, teams at Parasoft implement innovative testing strategies. Using service virtualization, they create stable mocks of LLMs, allowing repeatable tests while minimizing dependency on costly third-party services. Additionally, employing an “LLM judge” helps validate outputs semantically, even with variable phrasing. New protocols like Model Context Protocol (MCP) and Agent2Agent (A2A) further complicate testing workflows but can be effectively managed through automated testing tools like Parasoft SOAtest and Virtualize. These tools help teams ensure the reliability of evolving AI-infused applications, manage costs, and maintain quality. Adopting modern testing practices not only mitigates risks but also enables organizations to innovate confidently in the advanced AI landscape.
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