Debugging the Debuggers: Failure-Anchored Structured Recovery for Software Engineering Agents
- URL: http://arxiv.org/abs/2605.08717v1
- Date: Sat, 09 May 2026 06:02:08 GMT
- Title: Debugging the Debuggers: Failure-Anchored Structured Recovery for Software Engineering Agents
- Abstract summary: PROBE is a failure-anchored framework for structured recovery in software engineering agents.<n>We evaluate PROBE across three settings: repositorylevel software repair, enterprise workflow recovery, and AIOps service mitigation.
- Score: 26.16025682022058
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Software engineering agents are increasingly deployed in evaluable engineering environments, yet post-failure recovery remains costly, manual, and ad hoc. Existing systems expose traces or generate follow-up feedback, but they do not convert heterogeneous runtime evidence into grounded, bounded recovery guidance for a subsequent attempt. We present PROBE, a failure-anchored framework for structured recovery in software engineering agents. PROBE organizes failed-run telemetry into structured evidence, structured diagnosis, and bounded recovery guidance through a Telemetry Layer, a Diagnosis Layer, and a Guidance Gate. The Telemetry Layer preserves fine-grained runtime signals, the Diagnosis Layer fuses cross-signal evidence into grounded diagnoses, and the Guidance Gate produces diagnosis-derived guidance only when it is evidence-grounded, actionable, and within the scope of agent-side behavior. We evaluate PROBE across three settings: repository-level software repair, enterprise workflow recovery, and AIOps service mitigation. On 257 initially unresolved cases, PROBE achieves 65.37% Top-1 diagnosis accuracy and a 21.79% recovery rate, outperforming the strongest non-PROBE baseline by 43.58 and 12.45 percentage points. The results reveal a diagnosis-recovery gap: accurate diagnosis is necessary but insufficient unless translated into bounded guidance that a subsequent attempt can execute and verify. Beyond controlled evaluation, a Microsoft IcM prototype shows that PROBE can attach as a non-intrusive side channel to existing service-diagnosis workflows without changing the agent policy, toolset, or execution budget. These results suggest that telemetry-grounded, failure-anchored recovery can improve post-failure recoverability under realistic engineering constraints.
Related papers
- Beyond Fault Localization: A Trajectory-Level Study of LLM Agents for Microservice Root Cause Analysis [13.865176720288938]
We use a trajectory-level framework to evaluate root cause analysis (RCA) performance.<n>We find a disconnect between answer correctness and diagnostic quality.<n>We operationalize this taxonomy as DiagGuard, a two-stage defense-in-depth architecture.
arXiv Detail & Related papers (2026-08-21T17:13:45Z) - Diagnosis Before Recovery: Turning Agent Failures into Selective Self-Correction [22.654396704495934]
Development-set failures can recover part of the missing diagnostic substrate by deciding which recovery interventions are admissible.<n>We propose DARC, a diagnosis-guided recovery harness that profiles task-family failure modes, prunes mismatched interventions from a shared recovery library, and freezes a verifier-selected success-cost policy for deployment.
arXiv Detail & Related papers (2026-08-12T08:14:45Z) - FailForge: Distilling Procedural Competence from Persistent Failures into Code Agents [49.519184792774304]
FailForge is an agentic framework that converts failed rollouts into training signal.<n>FailForge recovers over 26% of previously failed instances at marginal additional cost.
arXiv Detail & Related papers (2026-08-09T08:22:57Z) - AgentDebugX: An Open-Source Toolkit for Failure Observability, Attribution, and Recovery in LLM Agents [83.37969790806069]
LLM agent failures are difficult to debug because the step where an error surfaces is often not the one that caused it.<n>We present Agent DebuggerX, an open-source framework that organizes debug as a closed loop of Detect, Attribute, Recover, and Rerun.<n>At its core, Deep Debugger performs multi-turn root-cause diagnosis through global trajectory understanding.
arXiv Detail & Related papers (2026-07-21T06:21:13Z) - AgentTether: Graph-Guided Diagnosis and Runtime Intervention for Reliable LLM Agent Operation [27.0859707906163]
AgentTether is a run-time repair framework that automates post-run diagnosis and guided recovery without modifying the underlying agent or environment.<n>It localizes failure-critical subtrajectories by combining an offline normal-behavior model with a run-local graph detector.<n>It can be deployed as an offline diagnostic-and-guidance tool or as an online repair layer.
arXiv Detail & Related papers (2026-07-07T13:40:31Z) - Can Large Language Models Generate Observability-Aware Code? [51.30941152293301]
We present a systematic study of observability in agent-generated systems.<n>Our results reveal a consistent gap between diagnostic semantics at the source level and fault signals at runtime.<n>We introduce an observability-oriented skill, which can serve as a guidance to improve both diagnostic semantics and fault-signal exposure.
arXiv Detail & Related papers (2026-07-07T03:20:52Z) - Can LLMs Really Recover Microservice Failures? A Recovery-Aware Evaluation of Diagnosis-to-Action Reasoning [48.22526014304726]
This paper presents R2Act, a recovery-action evaluation framework for post-diagnosis incident response.<n>R2Act defines an incident schema, quality gate, action-space representation, recovery-validity metrics, offline evaluator, and live-replay protocol.<n>We instantiate the framework as a benchmark dataset of 302 quality-audited incidents from system.
arXiv Detail & Related papers (2026-07-06T03:05:17Z) - Early Diagnosis of Wasted Computation in Multi-Agent LLM Systems via Failure-Aware Observability [8.036549927091286]
This paper introduces a failure-aware observability framework for diagnosing wasted computation in multi-agent LLM traces.<n>We instantiate the framework in a three- agent question-answering system and evaluate it on 165 GAIA validation traces under identical execution caps.
arXiv Detail & Related papers (2026-05-31T17:50:11Z) - 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) - Skill-RAG: Failure-State-Aware Retrieval Augmentation via Hidden-State Probing and Skill Routing [12.09179106162719]
We propose Skill-RAG, a failure-aware RAG framework that couples a hidden-state prober with a prompt-based skill router.<n> Experiments show that Skill-RAG substantially improves accuracy on hard cases persisting after multi-turn retrieval.
arXiv Detail & Related papers (2026-04-17T07:25:43Z) - ToolMisuseBench: An Offline Deterministic Benchmark for Tool Misuse and Recovery in Agentic Systems [0.0]
Tool using agents often fail for operational reasons even when language understanding is strong.<n>Common causes include invalid arguments, interface drift, weak recovery, and inefficient retry behavior.<n>We introduce ToolMisuseBench, an offline deterministic benchmark for evaluating tool misuse and recovery under explicit step, call, and retry budgets.
arXiv Detail & Related papers (2026-04-02T00:42:29Z) - TRAJEVAL: Decomposing Code Agent Trajectories for Fine-Grained Diagnosis [23.834704102474927]
Code agents can resolve GitHub issues, yet when they fail, current evaluation provides no visibility into where or why.<n>We introduce TRAJEVAL, a diagnostic framework that decomposes agent trajectories into three interpretable stages.<n>We validate that these diagnostics are predictive, achieving model-level Pass@1 prediction within 0.87-2.1% MAE.
arXiv Detail & Related papers (2026-03-25T05:27:03Z) - 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) - VIBEPASS: Can Vibe Coders Really Pass the Vibe Check? [46.85901599242161]
We evaluate two coupled tasks: emphFault-Triggering Test Generation (FT-Test) and emphFault-targeted Program Repair (FPR)<n>We find that fault-targeted reasoning does not scale with general coding ability.<n>Test-guided repair reveals a complementary insight: when self-generated tests successfully witness a fault, the resulting repair matches or outperforms repair guided by externally provided tests, but tests that fail to witness the fault actively degrade repair below unguided baselines.
arXiv Detail & Related papers (2026-03-16T21:14:28Z) - Automated Self-Testing as a Quality Gate: Evidence-Driven Release Management for LLM Applications [51.56484100374058]
We present an automated self-testing framework that introduces quality gates with evidence-based release decisions.<n>We evaluate the framework through a longitudinal case study of an internally deployed multi-agent conversational AI system.
arXiv Detail & Related papers (2026-03-13T20:44:15Z) - AgentRx: Diagnosing AI Agent Failures from Execution Trajectories [9.61742219198197]
We release a benchmark of 115 failed trajectories spanning structured API, incident management, and open-ended web/file tasks.<n>Each trajectory is annotated with a critical failure step and a category from a grounded-theory derived, cross-domain failure taxonomy.<n>We present AGENTRX, an automated domain-agnostic diagnostic framework that pinpoints the critical failure step in a failed agent trajectory.
arXiv Detail & Related papers (2026-02-02T18:54:07Z) - 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) - Where LLM Agents Fail and How They can Learn From Failures [62.196870049524364]
Large Language Model (LLM) agents have shown promise in solving complex, multi-step tasks.<n>They amplify vulnerability to cascading failures, where a single root-cause error propagates through subsequent decisions.<n>Current systems lack a framework that can comprehensively understand agent error in a modular and systemic way.<n>We introduce the AgentErrorTaxonomy, a modular classification of failure modes spanning memory, reflection, planning, action, and system-level operations.
arXiv Detail & Related papers (2025-09-29T18:20:27Z)
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.