  This paper revisits recent code similarity evaluation metrics, particularly
focusing on the application of Abstract Syntax Tree (AST) editing distance in
diverse programming languages. In particular, we explore the usefulness of
these metrics and compare them to traditional sequence similarity metrics. Our
experiments showcase the effectiveness of AST editing distance in capturing
intricate code structures, revealing a high correlation with established
metrics. Furthermore, we explore the strengths and weaknesses of AST editing
distance and prompt-based GPT similarity scores in comparison to BLEU score,
execution match, and Jaccard Similarity. We propose, optimize, and publish an
adaptable metric that demonstrates effectiveness across all tested languages,
representing an enhanced version of Tree Similarity of Edit Distance (TSED).
