Explaining the Unseen: Leveraging XAI to Enhance the Trustworthiness of Black-Box Models in Performance Testing

Abstract

This paper presents a novel approach to enhance the trustworthiness of black-box and interpretable models in performance testing through the application of Explainable Artificial Intelligence (XAI). The proposed approach utilizes the Shapley Additive Explanation (SHAP) algorithm as a surrogate model, enabling performance analysts to gain insights into the decision-making process of black-box machine learning models. By incorporating SHAP around black-box models, analysts can obtain explanations regarding the pass or fail status of a test, as well as identify the relative significance of performance data to the machine learning models. To evaluate the effectiveness of the proposed approach, load testing experiments were conducted on a real testbed, employing industry-standard benchmarks and manually injected performance bugs. The results demonstrate that the proposed approach significantly enhances the trustworthiness of machine learning models by providing interpretable explanations for their decision-making. Furthermore, the approach showcases its versatility across domains and requires minimal effort to operate, making it highly applicable in various contexts.