  Context: The identification of bugs within the reported issues in an issue
tracker is crucial for the triage of issues. Machine learning models have shown
promising results regarding the performance of automated issue type prediction.
However, we have only limited knowledge beyond our assumptions how such models
identify bugs. LIME and SHAP are popular technique to explain the predictions
of classifiers.
  Objective: We want to understand if machine learning models provide
explanations for the classification that are reasonable to us as humans and
align with our assumptions of what the models should learn. We also want to
know if the prediction quality is correlated with the quality of explanations.
  Method: We conduct a study where we rate LIME and SHAP explanations based on
their quality of explaining the outcome of an issue type prediction model. For
this, we rate the quality of the explanations themselves, i.e., if they align
with our expectations and if they help us to understand the underlying machine
learning model.
