Towards Effectively Leveraging Execution Traces for Program Repair with Code LLMs

• URL: http://arxiv.org/abs/2505.04441v1
• Date: Wed, 07 May 2025 14:12:41 GMT
• Title: Towards Effectively Leveraging Execution Traces for Program Repair with Code LLMs
• Authors: Mirazul Haque, Petr Babkin, Farima Farmahinifarahani, Manuela Veloso,
• Abstract summary: Large Language Models (LLMs) show promising performance on various programming tasks.<n>We aim to remedy this potential blind spot by augmenting standard APR prompts with program execution traces.
• Score: 13.708569727719434
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) show promising performance on various programming tasks, including Automatic Program Repair (APR). However, most approaches to LLM-based APR are limited to the static analysis of the programs, while disregarding their runtime behavior. Inspired by knowledge-augmented NLP, in this work, we aim to remedy this potential blind spot by augmenting standard APR prompts with program execution traces. We evaluate our approach using the GPT family of models on three popular APR datasets. Our findings suggest that simply incorporating execution traces into the prompt provides a limited performance improvement over trace-free baselines, in only 2 out of 6 tested dataset / model configurations. We further find that the effectiveness of execution traces for APR diminishes as their complexity increases. We explore several strategies for leveraging traces in prompts and demonstrate that LLM-optimized prompts help outperform trace-free prompts more consistently. Additionally, we show trace-based prompting to be superior to finetuning a smaller LLM on a small-scale dataset; and conduct probing studies reinforcing the notion that execution traces can complement the reasoning abilities of the LLMs.

Related papers

• Efficiency-Effectiveness Reranking FLOPs for LLM-based Rerankers [15.549112491482818]
  Large Language Models (LLMs) have been applied to reranking tasks in information retrieval, achieving strong performance.<n>Existing studies evaluate the efficiency of LLM-based rerankers using proxy metrics such as latency, the number of forward passes, input tokens, and output tokens.<n>We propose Etextsuperscript2R-FLOPs, for LLM-based rerankers: ranking metrics per PetaFLOP (RPP) for relevance per compute and queries per PetaFLOP (QPP) for hardware-agnostic throughput.
  arXiv  Detail & Related papers  (2025-07-08T17:56:28Z)
• Can Prompt Difficulty be Online Predicted for Accelerating RL Finetuning of Reasoning Models? [62.579951798437115]
  This work investigates iterative approximate evaluation for arbitrary prompts.<n>It introduces Model Predictive Prompt Selection (MoPPS), a Bayesian risk-predictive framework.<n>MoPPS reliably predicts prompt difficulty and accelerates training with significantly reduced rollouts.
  arXiv  Detail & Related papers  (2025-07-07T03:20:52Z)
• Optimising Language Models for Downstream Tasks: A Post-Training Perspective [0.0]
  Language models (LMs) have demonstrated remarkable capabilities in NLP.<n>But adapting them efficiently and robustly to specific tasks remains challenging.<n>This thesis proposes a series of methods to better adapt LMs to downstream applications.
  arXiv  Detail & Related papers  (2025-06-26T00:49:35Z)
• Prompt engineering and framework: implementation to increase code reliability based guideline for LLMs [0.0]
  We introduce a prompt template designed to improve the quality and correctness of generated code snippets.<n>We demonstrate that our approach outperforms widely studied zero-shot and Chain-of-Thought (CoT) methods in terms of the Pass@k metric.
  arXiv  Detail & Related papers  (2025-03-19T18:33:08Z)
• LLM Program Optimization via Retrieval Augmented Search [71.40092732256252]
  We propose a blackbox adaptation method called Retrieval Augmented Search (RAS) that performs beam search over candidate optimizations.<n>We show that RAS performs 1.8$times$ better than prior state-of-the-art blackbox adaptation strategies.<n>We also propose a method called AEGIS for improving interpretability by decomposing training examples into "atomic edits"
  arXiv  Detail & Related papers  (2025-01-31T06:34:47Z)
• Q*: Improving Multi-step Reasoning for LLMs with Deliberative Planning [53.6472920229013]
  Large Language Models (LLMs) have demonstrated impressive capability in many natural language tasks. LLMs are prone to produce errors, hallucinations and inconsistent statements when performing multi-step reasoning. We introduce Q*, a framework for guiding LLMs decoding process with deliberative planning.
  arXiv  Detail & Related papers  (2024-06-20T13:08:09Z)
• Synthesizing Programmatic Reinforcement Learning Policies with Large Language Model Guided Search [7.769411917500852]
  We introduce a novel LLM-guided search framework (LLM-GS)<n>Our key insight is to leverage the programming expertise and common sense reasoning of LLMs to enhance the efficiency of assumption-free, random-guessing search methods.<n>We develop a search algorithm named Scheduled Hill Climbing, designed to efficiently explore the programmatic search space to improve the programs consistently.
  arXiv  Detail & Related papers  (2024-05-26T06:33:48Z)
• 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)
• Are Large Language Models Good Prompt Optimizers? [65.48910201816223]
  We conduct a study to uncover the actual mechanism of LLM-based Prompt Optimization. Our findings reveal that the LLMs struggle to identify the true causes of errors during reflection, tending to be biased by their own prior knowledge. We introduce a new "Automatic Behavior Optimization" paradigm, which directly optimize the target model's behavior in a more controllable manner.
  arXiv  Detail & Related papers  (2024-02-03T09:48:54Z)
• LLMRec: Benchmarking Large Language Models on Recommendation Task [54.48899723591296]
  The application of Large Language Models (LLMs) in the recommendation domain has not been thoroughly investigated. We benchmark several popular off-the-shelf LLMs on five recommendation tasks, including rating prediction, sequential recommendation, direct recommendation, explanation generation, and review summarization. The benchmark results indicate that LLMs displayed only moderate proficiency in accuracy-based tasks such as sequential and direct recommendation.
  arXiv  Detail & Related papers  (2023-08-23T16:32:54Z)
• GPT is becoming a Turing machine: Here are some ways to program it [16.169056235216576]
  We show that GPT-3 models can be triggered to execute programs that involve loops. We show that prompts that may not even cover one full task example can trigger algorithmic behaviour.
  arXiv  Detail & Related papers  (2023-03-25T00:43:41Z)
• Guiding Large Language Models via Directional Stimulus Prompting [114.84930073977672]
  We introduce Directional Stimulus Prompting, a novel framework for guiding black-box large language models (LLMs) toward specific desired outputs. Instead of directly adjusting LLMs, our method employs a small tunable policy model to generate an auxiliary directional stimulus prompt for each input instance.
  arXiv  Detail & Related papers  (2023-02-22T17:44:15Z)

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.