Generalizability of Code Clone Detection on CodeBERT

2022 | PDF
Automated Software Engineering (ASE) Late-Breaking Results

Authors: Tim Sonnekalb and Bernd Gruner and Clemens-Alexander Brust and Patrick M├Ąder

Abstract: 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.

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