![]() When Tricentis NeoLoad evaluated the potential for an AI-augmented solution, we examined various facets of performance engineering (performance test modeling, design/scripting, test execution, and analysis) to determine where AI could be most beneficial. Often these goals appear to be precisely defined metrics - “the system must be able to handle 500 transactions per second” - but where do these benchmarks come from? What if users are performing other transactions at the same time? Can the system still handle 500 transactions per second? Empirical load requirements do not guarantee that the system will be able to handle the load in production, as they do not take into account other factors or how users actually use the system. Tests are built based on categorically quantified requirements set forth in SLAs.
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