@inproceedings{di-etal-2026-code,
title = "Code Reffix: A Benchmark for Reflection-Guided Code Repair with Large Language Models",
author = "Di, Zaiyuan and
Chen, Jianting and
Yang, Yunxiao and
Gao, Xiaoying and
Yang, Li and
Wang, Zhihao and
Xiang, Yang",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.762/",
pages = "15541--15561",
ISBN = "979-8-89176-395-1",
abstract = "While recent studies have increasingly emphasized the role of reflection in code repair tasks, existing benchmarks still target the repair generation capability of LLMs, lacking fine-grained evaluation of reflection generation capability. To this end, we propose Code Reffix, a benchmark featuring an automated pipeline with oracle reflections and a dual-task protocol to decouple the evaluation of reflection from repair. Through extensive experiments on 14 LLMs and fine-tuning analysis, we aim to pinpoint performance bottlenecks of code repair, quantify reflection quality, and verify the value of reflection optimization. Evaluations reveal that underperforming reflection capabilities of small-scale LLMs remain a major bottleneck for code repair. By quantifying this gap, Code Reffix provides a critical foundation for optimizing LLMs to achieve superior repair performance."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="di-etal-2026-code">
<titleInfo>
<title>Code Reffix: A Benchmark for Reflection-Guided Code Repair with Large Language Models</title>
</titleInfo>
<name type="personal">
<namePart type="given">Zaiyuan</namePart>
<namePart type="family">Di</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jianting</namePart>
<namePart type="family">Chen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yunxiao</namePart>
<namePart type="family">Yang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Xiaoying</namePart>
<namePart type="family">Gao</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Li</namePart>
<namePart type="family">Yang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Zhihao</namePart>
<namePart type="family">Wang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yang</namePart>
<namePart type="family">Xiang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Findings of the Association for Computational Linguistics: ACL 2026</title>
</titleInfo>
<name type="personal">
<namePart type="given">Maria</namePart>
<namePart type="family">Liakata</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Viviane</namePart>
<namePart type="given">P</namePart>
<namePart type="family">Moreira</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jiajun</namePart>
<namePart type="family">Zhang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">David</namePart>
<namePart type="family">Jurgens</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">San Diego, California, United States</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-395-1</identifier>
</relatedItem>
<abstract>While recent studies have increasingly emphasized the role of reflection in code repair tasks, existing benchmarks still target the repair generation capability of LLMs, lacking fine-grained evaluation of reflection generation capability. To this end, we propose Code Reffix, a benchmark featuring an automated pipeline with oracle reflections and a dual-task protocol to decouple the evaluation of reflection from repair. Through extensive experiments on 14 LLMs and fine-tuning analysis, we aim to pinpoint performance bottlenecks of code repair, quantify reflection quality, and verify the value of reflection optimization. Evaluations reveal that underperforming reflection capabilities of small-scale LLMs remain a major bottleneck for code repair. By quantifying this gap, Code Reffix provides a critical foundation for optimizing LLMs to achieve superior repair performance.</abstract>
<identifier type="citekey">di-etal-2026-code</identifier>
<location>
<url>https://aclanthology.org/2026.findings-acl.762/</url>
</location>
<part>
<date>2026-07</date>
<extent unit="page">
<start>15541</start>
<end>15561</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Code Reffix: A Benchmark for Reflection-Guided Code Repair with Large Language Models
%A Di, Zaiyuan
%A Chen, Jianting
%A Yang, Yunxiao
%A Gao, Xiaoying
%A Yang, Li
%A Wang, Zhihao
%A Xiang, Yang
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F di-etal-2026-code
%X While recent studies have increasingly emphasized the role of reflection in code repair tasks, existing benchmarks still target the repair generation capability of LLMs, lacking fine-grained evaluation of reflection generation capability. To this end, we propose Code Reffix, a benchmark featuring an automated pipeline with oracle reflections and a dual-task protocol to decouple the evaluation of reflection from repair. Through extensive experiments on 14 LLMs and fine-tuning analysis, we aim to pinpoint performance bottlenecks of code repair, quantify reflection quality, and verify the value of reflection optimization. Evaluations reveal that underperforming reflection capabilities of small-scale LLMs remain a major bottleneck for code repair. By quantifying this gap, Code Reffix provides a critical foundation for optimizing LLMs to achieve superior repair performance.
%U https://aclanthology.org/2026.findings-acl.762/
%P 15541-15561
Markdown (Informal)
[Code Reffix: A Benchmark for Reflection-Guided Code Repair with Large Language Models](https://aclanthology.org/2026.findings-acl.762/) (Di et al., Findings 2026)
ACL
- Zaiyuan Di, Jianting Chen, Yunxiao Yang, Xiaoying Gao, Li Yang, Zhihao Wang, and Yang Xiang. 2026. Code Reffix: A Benchmark for Reflection-Guided Code Repair with Large Language Models. In Findings of the Association for Computational Linguistics: ACL 2026, pages 15541–15561, San Diego, California, United States. Association for Computational Linguistics.