Revisiting Code Similarity Evaluation with Abstract Syntax Tree Edit Distance
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866909637572296704 |
|---|---|
| author | Song, Yewei Lothritz, Cedric Tang, Daniel Bissyandé, Tegawendé F. Klein, Jacques |
| author_facet | Song, Yewei Lothritz, Cedric Tang, Daniel Bissyandé, Tegawendé F. Klein, Jacques |
| contents | 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). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_08817 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Revisiting Code Similarity Evaluation with Abstract Syntax Tree Edit Distance Song, Yewei Lothritz, Cedric Tang, Daniel Bissyandé, Tegawendé F. Klein, Jacques Computation and Language Programming Languages Software Engineering 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). |
| title | Revisiting Code Similarity Evaluation with Abstract Syntax Tree Edit Distance |
| topic | Computation and Language Programming Languages Software Engineering |
| url | https://arxiv.org/abs/2404.08817 |