Related papers: PracRepair: LLM-Empowered Automated Program Repair Inspired by Human-Like Debugging Practices

PracRepair: LLM-Empowered Automated Program Repair Inspired by Human-Like Debugging Practices

URL: http://arxiv.org/abs/2606.17612v1
Date: Tue, 16 Jun 2026 07:18:37 GMT
Title: PracRepair: LLM-Empowered Automated Program Repair Inspired by Human-Like Debugging Practices
Abstract summary: textscPracRepair is a fully automated program repair framework inspired by human-like debug practices.<n>textscPracRepair consistently outperforms state-of-the-art baselines.<n>textscPracRepair generalizes effectively to RWB (Real-World Bugs)
Score: 22.432182416621917
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: As software systems grow in scale and complexity, debugging and repair remain costly and time-consuming. Large language models (LLMs) have advanced automated program repair (APR), but existing LLM-based APR approaches still largely rely on static or retrieved context, error messages, and coarse-grained validation outcomes. As a result, they underutilize dynamic information for failure understanding and repair, including failure-execution dynamics and patch-validation dynamics. Effectively leveraging such information, however, is challenging: failure-execution traces are large and noisy, raw static-dynamic context is not self-explanatory, and patch-validation dynamics are often reduced to coarse feedback. To address these challenges, we propose \textsc{PracRepair}, a fully automated LLM-based APR framework inspired by human-like debugging practices. \textsc{PracRepair} constructs an on-demand static-dynamic context from buggy programs and failure executions, performs question-driven failure diagnosis to formulate explicit repair hypotheses, and iteratively refines candidate patches using validation diagnostics and trace-level behavioral changes. Experimental results on Defects4J V1.2 and V2.0 show that \textsc{PracRepair} consistently outperforms state-of-the-art baselines. Specifically, under GPT-3.5, \textsc{PracRepair} correctly fixes 139/136 bugs on Defects4J V1.2/V2.0, while under GPT-4o it further improves to 162/171. Moreover, \textsc{PracRepair} generalizes effectively to RWB (Real-World Bugs), achieving the best performance across multiple foundation models.

Related papers

Beyond the Traceback: Using LLMs for Adaptive Explanations of Programming Errors [52.619793817715326]
We present a multi-stage crowdsourced study evaluating skill-targeted, LLM-generated Python error messages.<n>We show that while LLM-rewritten messages significantly improved subjective evaluations, these perceived gains did not translate into statistically significant improvements in objective debug performance.<n>This highlights a critical human-AI complementarity gap: explanations that feel better to users do not necessarily make them more effective debuggers.
arXiv Detail & Related papers (2026-08-21T09:14:28Z)
CausalRepair: Bridging the Causality Gap in Large Language Model-Based Automated Program Repair via Dual-Slicing [18.917995731082073]
CausalRepair is a conversation-driven APR framework based on minimal causal context.<n>It correctly fixes 313 bugs on Defects4J, outperforming state-of-the-art approaches such as ReinFix and TSAPR.
arXiv Detail & Related papers (2026-08-11T07:56:32Z)
LiveFMBench: Unveiling the Power and Limits of Agentic Workflows in Specification Generation [75.05397479715576]
Large language models (LLMs) and agents have shown promising progress, but their true capabilities and failure modes remain unclear.<n>We present the first systematic and contamination-aware study of LLM- and agent-based formal specification generation for C programs.
arXiv Detail & Related papers (2026-05-02T11:31:33Z)
DebugHarness: Emulating Human Dynamic Debugging for Autonomous Program Repair [8.290621289192048]
DebugHarness is an autonomous debug agent powered by the Large Language Model (LLM)<n>Instead of merely examining static code, DebugHarness actively queries the live runtime environment.<n>We evaluate DebugHarness on SEC-bench, a rigorous dataset of real-world C/C++ security vulnerabilities.
arXiv Detail & Related papers (2026-04-04T06:49:30Z)
Runtime Execution Traces Guided Automated Program Repair with Multi-Agent Debate [8.424102114588559]
Automated Program Repair (APR) struggles with complex logic errors and silent failures.<n>Current LLM-based APR methods are mostly static, relying on source code and basic test outputs.<n>We propose TraceRepair, a multi-agent framework that leverages runtime facts as shared constraints for patch validation.
arXiv Detail & Related papers (2026-04-03T02:23:25Z)
Dynamic analysis enhances issue resolution [53.50448142467294]
DAIRA (Dynamic Analysis-enhanced Issue Resolution Agent) is an automated repair framework that embeds dynamic analysis into the agent's reasoning cycle.<n> Driven by a Test Tracing-Driven methodology, DAIRA utilizes lightweight monitors to extract critical runtime data.<n>When powered by Gemini 3 Flash Preview, DAIRA establishes a new state-of-the-art (SOTA) performance, achieving a 79.4% resolution rate on the SWE-bench Verified dataset.
arXiv Detail & Related papers (2026-03-23T14:48:54Z)
DynaFix: Iterative Automated Program Repair Driven by Execution-Level Dynamic Information [20.300297868395454]
Automated Program Repair (APR) aims to automatically generate correct patches for buggy programs.<n>Recent approaches leveraging large language models (LLMs) have shown promise but face limitations.<n>We propose DynaFix, an execution-level dynamic information-driven APR method that iteratively leverages runtime information to refine the repair process.
arXiv Detail & Related papers (2025-12-31T05:13:34Z)
InspectCoder: Dynamic Analysis-Enabled Self Repair through interactive LLM-Debugger Collaboration [71.18377595277018]
Large Language Models (LLMs) frequently generate buggy code with complex logic errors that are challenging to diagnose.<n>We present InspectCoder, the first agentic program repair system that empowers LLMs to actively conduct dynamic analysis via interactive debugger control.
arXiv Detail & Related papers (2025-10-21T06:26:29Z)
How Can Input Reformulation Improve Tool Usage Accuracy in a Complex Dynamic Environment? A Study on $τ$-bench [58.114899897566964]
In a multi-turn conversational environment, large language models (LLMs) often struggle with consistent reasoning and adherence to domain-specific policies.<n>We propose the Input-Reformulation Multi-Agent (IRMA) framework, which automatically reformulates user queries augmented with relevant domain rules.<n>IRMA significantly outperforms ReAct, Function Calling, and Self-Reflection by 16.1%, 12.7%, and 19.1%, respectively.
arXiv Detail & Related papers (2025-08-28T15:57:33Z)
RePaCA: Leveraging Reasoning Large Language Models for Static Automated Patch Correctness Assessment [0.0]
We introduce RePaCA, a novel static APCA technique that leverages Large Language Models (LLMs) specialized in thinking tasks.<n>Our approach achieves state-of-the-art performance, with 83.1% accuracy and an 84.8% F1-score.
arXiv Detail & Related papers (2025-07-30T11:21:09Z)
Specification-Guided Repair of Arithmetic Errors in Dafny Programs using LLMs [79.74676890436174]
We present an APR tool for Dafny that uses formal specifications as oracles for fault localization and repair.<n>We localize faults through a series of steps, which include using Hoare logic to determine the state of each statement within the program.<n>Our tool achieves 89.6% fault localization coverage and GPT-4o mini yields the highest repair success rate of 74.18%.
arXiv Detail & Related papers (2025-07-04T15:36:12Z)
Towards Practical and Useful Automated Program Repair for Debugging [4.216808129651161]
PracAPR is an interactive repair system that works in an Integrated Development Environment (IDE) PracAPR does not require a test suite or program re-execution.
arXiv Detail & Related papers (2024-07-12T03:19:54Z)

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