  Transformer networks such as CodeBERT already achieve outstanding results for
code clone detection in benchmark datasets, so one could assume that this task
has already been solved. However, code clone detection is not a trivial task.
Semantic code clones, in particular, are challenging to detect. We show that
the generalizability of CodeBERT decreases by evaluating two different subsets
of Java code clones from BigCloneBench. We observe a significant drop in F1
score when we evaluate different code snippets and functionality IDs than those
used for model building.
