  Diagnosis results are highly dependent on the volume of test set. To derive
the most efficient test set, we propose several machine learning based methods
to predict the minimum amount of test data that produces relatively accurate
diagnosis. By collecting outputs from failing circuits, the feature matrix and
label vector are generated, which involves the inference information of the
test termination point. Thus we develop a prediction model to fit the data and
determine when to terminate testing. The considered methods include LASSO and
Support Vector Machine(SVM) where the relationship between goals(label) and
predictors(feature matrix) are considered to be linear in LASSO and nonlinear
in SVM. Numerical results show that SVM reaches a diagnosis accuracy of 90.4%
while deducting the volume of test set by 35.24%.
