Computer Science > Computation and Language
arXiv:2404.08817 (cs)
[Submitted on 12 Apr 2024 (v1), last revised 3 Jun 2024 (this version, v2)]
Title:Revisiting Code Similarity Evaluation with Abstract Syntax Tree Edit Distance
View a PDF of the paper titled Revisiting Code Similarity Evaluation with Abstract Syntax Tree Edit Distance, by Yewei Song and 4 other authors
View PDFHTML (experimental)Abstract: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).
| Comments: | ACL 2024 Main |
| Subjects: | Computation and Language (cs.CL); Programming Languages (cs.PL); Software Engineering (cs.SE) |
| Cite as: | arXiv:2404.08817 [cs.CL] |
| (orarXiv:2404.08817v2 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2404.08817 arXiv-issued DOI via DataCite |
Submission history
From: Yewei Song [view email][v1] Fri, 12 Apr 2024 21:28:18 UTC (7,981 KB)
[v2] Mon, 3 Jun 2024 11:56:38 UTC (7,983 KB)
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View a PDF of the paper titled Revisiting Code Similarity Evaluation with Abstract Syntax Tree Edit Distance, by Yewei Song and 4 other authors
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