Papers by Xunzhu Tang
Revisiting Code Similarity Evaluation with Abstract Syntax Tree Edit Distance (2024.acl-short)
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| Challenge: | Abstract Syntax Tree (AST) editing distance is a new evaluation metric for code generation tasks. |
| Approach: | They propose, optimize, and publish an enhanced version of Tree Similarity of Edit Distance (TSED) based on AST editing distance and prompt-based GPT similarity scores. |
| Outcome: | The proposed metric is an enhanced version of Tree Similarity of Edit Distance (TSED) it is compared to BLEU score, execution match, and Jaccard similarity across languages. |
SynFix: Dependency-Aware Program Repair via RelationGraph Analysis (2025.findings-acl)
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Xunzhu Tang, Jiechao Gao, Jin Xu, Tiezhu Sun, Yewei Song, Saad Ezzini, Wendkûuni C. Ouédraogo, Jacques Klein, Tegawendé F. Bissyandé
| Challenge: | Existing methods for resolving repository-level debugging are limited by their interdependencies. |
| Approach: | They propose a RelationGraph-based approach that integrates large language models with structural search and synchronization techniques for coordinated program repair across codebases. |
| Outcome: | SynFix resolves 52.33% of issues in SWE-bench-lite, 55.8% in Swe-bech-verified and 29.86% in S WE-beach-full. |
CodeAgent: Autonomous Communicative Agents for Code Review (2024.emnlp-main)
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Xunzhu Tang, Kisub Kim, Yewei Song, Cedric Lothritz, Bei Li, Saad Ezzini, Haoye Tian, Jacques Klein, Tegawendé Bissyandé
| Challenge: | Existing methods for code review rely on single input-output generative models and thus lack the collaborative nature of code review. |
| Approach: | They propose a multi-agent Large Language Model (LLM) system for code review automation that incorporates a supervisory agent to ensure that all the agents’ contributions address the initial review question. |
| Outcome: | The proposed system detects inconsistencies between code changes and commit messages, identify vulnerabilities, validates code style adherence, and suggests code revisions. |