Revisiting Feedback-Driven LLM Code Repair: A Replication and Exploratory Java Extension

• URL: http://arxiv.org/abs/2609.00362v1
• Date: Mon, 31 Aug 2026 20:55:41 GMT
• Title: Revisiting Feedback-Driven LLM Code Repair: A Replication and Exploratory Java Extension
• Abstract summary: This study partially replicates the original study on 394 repair tasks using GPT-4o and Claude 3.5 Sonnet.<n>We construct 100 erroneous repair instances from 50 Java tasks and evaluate feedback effectiveness.<n>Our results show that previous conclusions from Python may be sensitive to benchmark construction, feedback representation, and tooling ecosystem.
• Score: 5.124343690857496
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Since the advent of Large Language Models (LLMs), practitioners have increasingly leveraged them to support their software engineering tasks, including automated code repair, showing promising results. Yet, concerns regarding reproducibility and generalizability remain largely unexplored. To further evaluate these concerns and associated impacts, we partially reproduce and conduct an exploratory Java extension of the FeedbackEval benchmark [1], which evaluates how LLMs leverage different feedback types for Python code repair. First, we partially replicate the original study on 394 repair tasks using GPT-4o and Claude 3.5 Sonnet, reproducing and observing the main qualitative trends reported in the original work. Second, we conduct an exploratory Java extension by constructing 100 erroneous repair instances from 50 Java tasks and evaluating feedback effectiveness. Our results show that previous conclusions from Python may be sensitive to benchmark construction, feedback representation, and tooling ecosystem, motivating more controlled multilingual benchmarks. Specifically, while test feedback remains the strongest feedback type in our Python replication, the same ranking is not observed in our Java extension, as simple and JUnit-based test feedback do not differ significantly. We hypothesize that differences in feedback informativeness and tooling ecosystems, such as the verbosity of test frameworks, may partly explain such a difference. Finally, lighter prompts reduce cost without significant differences in repair effectiveness. Overall, our findings confirm key trends under a partially controlled replication and highlight the need for more rigorous multilingual evaluation and careful feedback design in LLMbased repair systems.

Related papers

• Chain-of-Experience for Continual LLM Improvement [57.11645184182825]
  We study how large language models learn from iterative experience at test time.<n>We instantiate Chain-of-Experience (CoE) with diverse feedback mechanisms.<n>Our study shows that leveraging iterative experience consistently outperforms feedback-free baselines.
  arXiv  Detail & Related papers  (2026-08-18T17:22:54Z)
• The Unlearning Mirage: A Dynamic Framework for Evaluating LLM Unlearning [54.67958855362658]
  We propose a dynamic framework that stress tests unlearning using complex structured queries.<n>Our approach first elicits knowledge from the target model (pre-unlearning) and constructs targeted probes, ranging from simple queries to multi-hop chains.<n>Our framework enables practical and scalable evaluation of unlearning methods without the need for manual construction of forget test sets.
  arXiv  Detail & Related papers  (2026-03-11T19:51:33Z)
• CompassVerifier: A Unified and Robust Verifier for LLMs Evaluation and Outcome Reward [50.97588334916863]
  We develop CompassVerifier, an accurate and robust lightweight verifier model for evaluation and outcome reward.<n>It demonstrates multi-domain competency spanning math, knowledge, and diverse reasoning tasks, with the capability to process various answer types.<n>We introduce VerifierBench benchmark comprising model outputs collected from multiple data sources, augmented through manual analysis of metaerror patterns to enhance CompassVerifier.
  arXiv  Detail & Related papers  (2025-08-05T17:55:24Z)
• Turning the Tide: Repository-based Code Reflection [52.13709676656648]
  We introduce LiveRepoReflection, a benchmark for evaluating code understanding and generation in multi-file repository contexts.<n>1,888 rigorously filtered test cases across $6$ programming languages to ensure diversity, correctness, and high difficulty.<n>We also create RepoReflection-Instruct, a large-scale, quality-filtered instruction-tuning dataset derived from diverse sources.
  arXiv  Detail & Related papers  (2025-07-14T02:36:27Z)
• Empirical Evaluation of Generalizable Automated Program Repair with Large Language Models [4.757323827658957]
  Automated Program Repair proposes bug fixes to aid developers in maintaining software.<n>Recent works have shown that LLMs can be used to generate repairs.<n>We evaluate a diverse set of 13 recent models, including open ones (e.g., Llama 3.3, Qwen 2.5 Coder, and DeepSeek R1 (dist.)) and closed ones (e.g., o3-mini, GPT-4o, Claude 3.7 Sonnet, Gemini 2.0 Flash)
  arXiv  Detail & Related papers  (2025-06-03T18:15:14Z)
• FeedbackEval: A Benchmark for Evaluating Large Language Models in Feedback-Driven Code Repair Tasks [28.849481030601666]
  We introduce FeedbackEval, a benchmark for evaluating large language models' feedback comprehension and performance.<n>We conduct a comprehensive empirical study on five state-of-the-art LLMs, including GPT-4o, Claude-3.5, Gemini-1.5, GLM-4, and Qwen2.5.<n>Our results show that structured feedback, particularly in the form of test feedback, leads to the highest repair success rates, while unstructured feedback proves significantly less effective.
  arXiv  Detail & Related papers  (2025-04-09T14:43:08Z)
• Automated Refactoring of Non-Idiomatic Python Code: A Differentiated Replication with LLMs [54.309127753635366]
  We present the results of a replication study in which we investigate GPT-4 effectiveness in recommending and suggesting idiomatic actions.<n>Our findings underscore the potential of LLMs to achieve tasks where, in the past, implementing recommenders based on complex code analyses was required.
  arXiv  Detail & Related papers  (2025-01-28T15:41:54Z)
• Re-ReST: Reflection-Reinforced Self-Training for Language Agents [101.22559705696885]
  Self-training in language agents can generate supervision from the agent itself.<n>We present Reflection-Reinforced Self-Training (Re-ReST), which uses a textitreflector to refine low-quality generated samples.
  arXiv  Detail & Related papers  (2024-06-03T16:21:38Z)
• A Case Study of LLM for Automated Vulnerability Repair: Assessing Impact of Reasoning and Patch Validation Feedback [7.742213291781287]
  We present VRpilot, a vulnerability repair technique based on reasoning and patch validation feedback. Our results show that VRpilot generates, on average, 14% and 7.6% more correct patches than the baseline techniques on C and Java.
  arXiv  Detail & Related papers  (2024-05-24T16:29:48Z)
• ContrastRepair: Enhancing Conversation-Based Automated Program Repair via Contrastive Test Case Pairs [23.419180504723546]
  ContrastRepair is a novel APR approach that augments conversation-driven APR by providing contrastive test pairs. We evaluate ContrastRepair on multiple benchmark datasets, including Defects4j, QuixBugs, and HumanEval-Java.
  arXiv  Detail & Related papers  (2024-03-04T12:15:28Z)
• FactCHD: Benchmarking Fact-Conflicting Hallucination Detection [64.4610684475899]
  FactCHD is a benchmark designed for the detection of fact-conflicting hallucinations from LLMs. FactCHD features a diverse dataset that spans various factuality patterns, including vanilla, multi-hop, comparison, and set operation. We introduce Truth-Triangulator that synthesizes reflective considerations by tool-enhanced ChatGPT and LoRA-tuning based on Llama2.
  arXiv  Detail & Related papers  (2023-10-18T16:27:49Z)
• Towards Reliable and Fluent Large Language Models: Incorporating Feedback Learning Loops in QA Systems [10.58737969057445]
  We build a dataset to train a critic model capable of evaluating the citation, correctness, and fluency of responses generated by large language models. We propose an automated feedback mechanism that leverages the critic model to offer real-time feedback on heterogeneous aspects of generated text. Experimental results demonstrate the efficacy of our approach, including a 4% precision increase in citation and an approximately 8% enhancement in the MAUVE metric for fluency.
  arXiv  Detail & Related papers  (2023-09-08T09:39:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.