Sensitivity Analysis for Test Resource Allocation

Abstract

To predict the response of a system with unknown parameters, a common route is to quantify the parameters using test data and propagate the results through a computational model of the system. Activities in this process may include model calibration and/or model validation. Data uncertainty has a significant effect on model calibration and model validation, and therefore affects the response prediction. Data uncertainty includes the uncertainty regarding the amount of data and numerical values of data. Although its effect can be qualitatively observed by trying different data sets and visually comparing the response predictions, a quantitative methodology assessing the contributions of these two types of data uncertainty to the uncertainty in the response prediction is necessary in order to solve test resource allocation problems. In this paper, a novel computational technique based on pseudo-random numbers is proposed to efficiently quantify the uncertainty in the data value of each type of test. Then the method of auxiliary variable based on the probability integral transform theorem is applied to build a deterministic function so that variance-based global sensitivity analysis can be conducted. The resultant global sensitivity indices quantify the contribution of data value uncertainty of each type of test to the uncertainty in the response prediction. Thus a methodology for robust test resource allocation is proposed, i.e., quantifying the number of each type of tests so that the response predictions using different data set are consistent.