Runtime Execution Traces Guided Automated Program Repair with Multi-Agent Debate

• URL: http://arxiv.org/abs/2604.02647v1
• Date: Fri, 03 Apr 2026 02:23:25 GMT
• Title: Runtime Execution Traces Guided Automated Program Repair with Multi-Agent Debate
• Abstract summary: 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.
• Score: 8.424102114588559
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automated Program Repair (APR) struggles with complex logic errors and silent failures. Current LLM-based APR methods are mostly static, relying on source code and basic test outputs, which fail to accurately capture complex runtime behaviors and dynamic data dependencies. While incorporating runtime evidence like execution traces exposes concrete state transitions, a single LLM interpreting this in isolation often overfits to specific hypotheses, producing patches that satisfy tests by coincidence rather than correct logic. Therefore, runtime evidence should act as objective constraints rather than mere additional input. We propose TraceRepair, a multi-agent framework that leverages runtime facts as shared constraints for patch validation. A probe agent captures execution snapshots of critical variables to form an objective repair basis. Meanwhile, a committee of specialized agents cross-verifies candidate patches to expose inconsistencies and iteratively refine them. Evaluated on the Defects4J benchmark, TraceRepair correctly fixes 392 defects, substantially outperforming existing LLM-based approaches. Extensive experiments demonstrate improved efficiency and strong generalization on a newly constructed dataset of recent bugs, confirming that performance gains arise from dynamic reasoning rather than memorization.

Related papers

• PhyAgentOS: A Self-Evolving Operating System for Embodied Agents with Decoupled Cognitive Planning and Physical Execution [49.776611937968]
  We present PhyAgentOS, a foundation delivering scheduling, verification, memory, benchmarking, and safety as system-level services.<n>Its Session-Centered treats a session, not an action, as the minimum unit of scheduling, compatibility, supervised execution, evidence collection, and acceptance.<n>SessionVerifier distinguishes execution termination from semantic task completion via evidence-grounded verdicts of success, failure, or replan.<n> Benchmarking reuses the deployment session and verification path, so results trace to real execution.
  arXiv  Detail & Related papers  (2026-07-18T04:46:53Z)
• TTHE: Test-Time Harness Evolution [50.26245555541721]
  Existing approaches optimize such harnesses before deployment, searching training or development data for a fixed agent workflow that is then frozen at test time.<n>We ask whether the harness can instead be optimized during evaluation itself, using only the unlabeled execution traces the agent produces on the test inputs.<n>During evaluation, TTHE maintains a population of candidate harnesses and refines them through an agentic proposer that reasons over their execution traces.<n>A judge then commits an improved harness from execution-derived proxy signals, and the selected program persists to govern subsequent inputs.
  arXiv  Detail & Related papers  (2026-07-09T05:53:39Z)
• PracRepair: LLM-Empowered Automated Program Repair Inspired by Human-Like Debugging Practices [22.432182416621917]
  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)
  arXiv  Detail & Related papers  (2026-06-16T07:18:37Z)
• CausalFlow: Causal Attribution and Counterfactual Repair for LLM Agent Failures [4.061481215870679]
  We introduce CausalFlow, an interventional framework that converts failed agent traces into minimal counterfactual repairs and reusable supervision.<n>CaulFlow supports two complementary uses: targeted test-time repair that recovers from failures with minimal behavioral drift, and training-time supervision suitable for offline preference optimization or reward modeling.
  arXiv  Detail & Related papers  (2026-05-25T01:47:01Z)
• 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)
• Detect--Repair--Verify for LLM-Generated Code: A Multi-Language, Multi-Granularity Empirical Study [10.18490328199727]
  Large language models can generate runnable software artifacts, but their security remains difficult to evaluate end to end.<n>This study examines that problem through a Detect--Repair--Verify (DRV) workflow, in which vulnerabilities are detected, repaired, and then rechecked with security and functional tests.<n>It addresses four gaps in current evidence: the lack of test-grounded benchmarks for LLM-generated artifacts, limited evidence on pipeline-level effectiveness, unclear reliability of detection reports as repair guidance, and uncertain repair trustworthiness under verification.
  arXiv  Detail & Related papers  (2026-03-24T18:18:30Z)
• 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)
• TraceCoder: A Trace-Driven Multi-Agent Framework for Automated Debugging of LLM-Generated Code [11.207330722400764]
  We present TraceCoder, a framework that emulates the observe-analyze-repair process of human experts.<n>The framework first instruments the code with diagnostic probes to capture fine-grained runtime traces.<n>It then conducts causal analysis on these traces to accurately identify the root cause of the failure.
  arXiv  Detail & Related papers  (2026-02-06T16:59:48Z)
• DoVer: Intervention-Driven Auto Debugging for LLM Multi-Agent Systems [48.971606069204825]
  DoVer is an intervention-driven debug framework for large language model (LLM)-based multi-agent systems.<n>It augments hypothesis generation with active verification through targeted interventions.<n>DoVer flips 18-28% of failed trials into successes, achieves up to 16% milestone progress, and validates or refutes 30-60% of failure hypotheses.
  arXiv  Detail & Related papers  (2025-12-07T09:23:48Z)
• 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)
• 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)

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.