Reiter's HS-Tree is one of the most popular diagnostic search algorithms due to its desirable properties and general applicability.
As initial system observations usually do not suffice to deterministically pin down just one explanation of the system's misbehavior, additional system measurements can help to differentiate between possible explanations.
To this end, Sequential Diagnosis approaches ask an oracle for additional system measurements.
In this work we present strategies for (optimal) measurement selection in model-based sequential diagnosis.
During query computation, existing sequential diagnosis methods often require the generation of many unnecessary query candidates and strongly rely on expensive logical reasoners.
Model-based diagnostic reasoning often leads to a large number of diagnostic hypotheses.
We report an experimental comparison of the performance of the two approaches to troubleshooting, specifically to test selection for fault diagnosis.