Transformer networks such as CodeBERT already achieve very good 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 difficult 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 of F1 score when we evaluate different code snippets and different functionality IDs than those used for model building.
Generalizability of Code Clone Detection on CodeBERT
2022 ; Michigan, USA
10.10.2022
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Clone detection in automotive model-based development
Tema Archiv | 2008
|An evaluation of the cross-national generalizability of organizational commitment
Online Contents | 2008
|