Diagnostic testing is an oft-recommended use of sensitivity analysis to assess correctness or plausibility of model behavior. In this paper we demonstrate the use of sensitivity analysis as a complementary first-pass software test for the validation of model behavior. Typical testing processes rely on comparing model outputs to results known to be correct. Such approaches are limited to specific model configurations and require that correct results be known in advance. Property-based Sensitivity Analysis (PbSA) examines model properties in terms of the behavior of parameter sensitivities to provide a line of evidence that the expected conceptual relationships between model factors and their effects are present. Unanticipated results can indicate issues to be corrected. The PbSA approach is also scalable as it can complement existing testing practices and be applied in conjunction with global sensitivity methods that can reuse existing model evaluations or are otherwise independent of the sampling scheme.

诊断测试通常建议使用敏感性分析来评估模型行为的正确性或合理性。在本文中，我们演示了灵敏度分析作为模型行为验证的补充首过软件测试的用途。典型的测试过程依赖于将模型输出与已知正确的结果进行比较。这样的方法限于特定的模型配置，并且要求事先知道正确的结果。基于属性的敏感性分析（PbSA）根据参数敏感性的行为检查模型属性，以提供一系列证据表明模型因素及其影响之间存在预期的概念关系。出乎意料的结果可能表明有待纠正的问题。