mlsanityData to 14 Oct 2026

Debugging the Debuggers: Failure-Anchored Structured Recovery for Software Engineering Agents

arXiv 2605.08717 ↗First posted 9 May 2026cs.SE cs.AIcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 23 of its 50 references that are in the map. First posted on arXiv 9 May 2026.

Builds on

23 of 50 references.

References · 23

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  2. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,276
  3. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
  4. 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,442
  5. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,373
  6. 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,988
  7. 2023Gorilla: Large Language Model Connected with Massive APIscited by 1,332
  8. 2023Teaching Large Language Models to Self-Debugcited by 988
  9. 2023API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMscited by 591
  10. 2020A Unifying Review of Deep and Shallow Anomaly Detectioncited by 425
  11. 2024AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agentscited by 401
  12. 2024RepairAgent: An Autonomous, LLM-Based Agent for Program Repaircited by 232
11 more
  1. 2023Is Self-Repair a Silver Bullet for Code Generation?cited by 197
  2. 2023CodePlan: Repository-level Coding using LLMs and Planningcited by 140
  3. 2023OpenAgents: An Open Platform for Language Agents in the Wildcited by 137
  4. 2023Keep the Conversation Going: Fixing 162 out of 337 bugs for 0.42 each using ChatGPTcited by 125
  5. 2023Automatic Root Cause Analysis via Large Language Models for Cloud Incidentscited by 84
  6. 2025Understanding Software Engineering Agents: A Study of Thought-Action-Result Trajectoriescited by 81
  7. 2026AgentRx: Diagnosing AI Agent Failures from Execution Trajectoriescited by 60
  8. 2025MCP-AgentBench: Evaluating Real-World Language Agent Performance with MCP-Mediated Toolscited by 32
  9. 2024Automated Root Causing of Cloud Incidents using In-Context Learning with GPT-4cited by 28
  10. 2024X-lifecycle Learning for Cloud Incident Management using LLMscited by 19
  11. 2025Beyond Final Code: A Process-Oriented Error Analysis of Software Development Agents in Real-World GitHub Scenarioscited by 14

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 4.

4 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2026AgentDebugX: An Open-Source Toolkit for Failure Observability, Attribution, and Recovery in LLM Agents
  2. 2026EviACT: An Evidence-to-Action Framework for Agentic Program Repair
  3. 2026Diagnosis Before Recovery: Turning Agent Failures into Selective Self-Correction
  4. 2026SWE-Gate: Passing Functional Tests Is Not Enough for Software Engineering Agents
  5. 2026ContraFix: Skill-Enhanced Contrastive Runtime Analysis for Vulnerability Repair
  6. 2025InspectCoder: Dynamic Analysis-Enabled Self Repair through interactive LLM-Debugger Collaboration
  7. 2026Evaluating Agentic Code Repair Capabilities in Distributed Systems
  8. 2025Understanding Automated Program Repair Agents Through the Lens of Traceability: An Empirical Study