From Empirical Evaluation to Context-Aware Enhancement: Repairing Regression Errors with LLMs

• URL: http://arxiv.org/abs/2506.13182v1
• Date: Mon, 16 Jun 2025 07:49:18 GMT
• Title: From Empirical Evaluation to Context-Aware Enhancement: Repairing Regression Errors with LLMs
• Authors: Anh Ho, Thanh Le-Cong, Bach Le, Christine Rizkallah,
• Abstract summary: We conduct an empirical study of APR techniques on Java regression bugs.<n>RegMiner4APR is a benchmark of Java regression bugs collected from 32 widely used real-world Java GitHub repositories.<n>Our results show that classical APR tools fail to repair any bugs, while LLM-based APR approaches exhibit promising potential.
• Score: 1.078085076551721
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: [...] Since then, various APR approaches, especially those leveraging the power of large language models (LLMs), have been rapidly developed to fix general software bugs. Unfortunately, the effectiveness of these advanced techniques in the context of regression bugs remains largely unexplored. This gap motivates the need for an empirical study evaluating the effectiveness of modern APR techniques in fixing real-world regression bugs. In this work, we conduct an empirical study of APR techniques on Java regression bugs. To facilitate our study, we introduce RegMiner4APR, a high-quality benchmark of Java regression bugs integrated into a framework designed to facilitate APR research. The current benchmark includes 99 regression bugs collected from 32 widely used real-world Java GitHub repositories. We begin by conducting an in-depth analysis of the benchmark, demonstrating its diversity and quality. Building on this foundation, we empirically evaluate the capabilities of APR to regression bugs by assessing both traditional APR tools and advanced LLM-based APR approaches. Our experimental results show that classical APR tools fail to repair any bugs, while LLM-based APR approaches exhibit promising potential. Motivated by these results, we investigate impact of incorporating bug-inducing change information into LLM-based APR approaches for fixing regression bugs. Our results highlight that this context-aware enhancement significantly improves the performance of LLM-based APR, yielding 1.8x more successful repairs compared to using LLM-based APR without such context.

Related papers

• APRMCTS: Improving LLM-based Automated Program Repair with Iterative Tree Search [6.314858275160081]
  APRMCTS incorporates Monte Carlo Tree Search (MCTS) into patch searching by performing a global evaluation of the explored patches and selecting the most promising one for subsequent refinement and generation.<n>Our experiments on 835 bugs from Defects4J demonstrate that, when integrated with GPT-3.5, APRMCTS can fix a total of 201 bugs, which outperforms all state-of-the-art baselines.
  arXiv  Detail & Related papers  (2025-07-02T15:44:12Z)
• Taming Polysemanticity in LLMs: Provable Feature Recovery via Sparse Autoencoders [50.52694757593443]
  Existing SAE training algorithms often lack rigorous mathematical guarantees and suffer from practical limitations.<n>We first propose a novel statistical framework for the feature recovery problem, which includes a new notion of feature identifiability.<n>We introduce a new SAE training algorithm based on bias adaptation'', a technique that adaptively adjusts neural network bias parameters to ensure appropriate activation sparsity.
  arXiv  Detail & Related papers  (2025-06-16T20:58:05Z)
• ReLearn: Unlearning via Learning for Large Language Models [64.2802606302194]
  We propose ReLearn, a data augmentation and fine-tuning pipeline for effective unlearning.<n>This framework introduces Knowledge Forgetting Rate (KFR) and Knowledge Retention Rate (KRR) to measure knowledge-level preservation.<n>Our experiments show that ReLearn successfully achieves targeted forgetting while preserving high-quality output.
  arXiv  Detail & Related papers  (2025-02-16T16:31:00Z)
• An Early FIRST Reproduction and Improvements to Single-Token Decoding for Fast Listwise Reranking [50.81324768683995]
  FIRST is a novel approach that integrates a learning-to-rank objective and leveraging the logits of only the first generated token. We extend the evaluation of FIRST to the TREC Deep Learning datasets (DL19-22), validating its robustness across diverse domains. Our experiments confirm that fast reranking with single-token logits does not compromise out-of-domain reranking quality.
  arXiv  Detail & Related papers  (2024-11-08T12:08:17Z)
• LLMs-as-Instructors: Learning from Errors Toward Automating Model Improvement [93.38736019287224]
  "LLMs-as-Instructors" framework autonomously enhances the training of smaller target models. Inspired by the theory of "Learning from Errors", this framework employs an instructor LLM to meticulously analyze the specific errors within a target model. Within this framework, we implement two strategies: "Learning from Error," which focuses solely on incorrect responses to tailor training data, and "Learning from Error by Contrast", which uses contrastive learning to analyze both correct and incorrect responses for a deeper understanding of errors.
  arXiv  Detail & Related papers  (2024-06-29T17:16:04Z)
• Hybrid Automated Program Repair by Combining Large Language Models and Program Analysis [12.7034916462208]
  Automated Program Repair (APR) has garnered significant attention due to its potential to streamline the bug repair process for human developers. This paper introduces an innovative APR approach called GIANTREPAIR. Based on this insight, GIANTREPAIR first constructs patch skeletons from LLM-generated patches to confine the patch space, and then generates high-quality patches tailored to specific programs.
  arXiv  Detail & Related papers  (2024-06-03T05:05:12Z)
• 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)
• Regression-aware Inference with LLMs [52.764328080398805]
  We show that an inference strategy can be sub-optimal for common regression and scoring evaluation metrics. We propose alternate inference strategies that estimate the Bayes-optimal solution for regression and scoring metrics in closed-form from sampled responses.
  arXiv  Detail & Related papers  (2024-03-07T03:24:34Z)
• MR-GSM8K: A Meta-Reasoning Benchmark for Large Language Model Evaluation [60.65820977963331]
  We introduce a novel evaluation paradigm for Large Language Models (LLMs) This paradigm shifts the emphasis from result-oriented assessments, which often neglect the reasoning process, to a more comprehensive evaluation. By applying this paradigm in the GSM8K dataset, we have developed the MR-GSM8K benchmark.
  arXiv  Detail & Related papers  (2023-12-28T15:49:43Z)
• Practical Program Repair via Preference-based Ensemble Strategy [28.176710503313895]
  We propose a Preference-based Ensemble Program Repair framework (P-EPR) to rank APR tools for repairing different bugs. P-EPR is the first non-learning-based APR ensemble method that is novel in its exploitation of repair patterns. Experimental results show that P-EPR outperforms existing strategies significantly both in flexibility and effectiveness.
  arXiv  Detail & Related papers  (2023-09-15T07:23:04Z)
• Revisiting the Plastic Surgery Hypothesis via Large Language Models [13.488029636215089]
  We propose FitRepair, which combines the direct usage of Large Language Models with two domain-specific fine-tuning strategies and one prompting strategy for more powerful APR.<n>Our experiments on the widely studied Defects4j 1.2 and 2.0 datasets show that FitRepair fixes 89 and 44 bugs.
  arXiv  Detail & Related papers  (2023-03-18T20:33:46Z)
• Regression Bugs Are In Your Model! Measuring, Reducing and Analyzing Regressions In NLP Model Updates [68.09049111171862]
  This work focuses on quantifying, reducing and analyzing regression errors in the NLP model updates. We formulate the regression-free model updates into a constrained optimization problem. We empirically analyze how model ensemble reduces regression.
  arXiv  Detail & Related papers  (2021-05-07T03:33:00Z)

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.