mlsanityData to 14 Oct 2026

FixItFlow: Automated Troubleshooting Guide Generation from Cloud Incidents

arXiv 2607.13035 ↗First posted 3 May 2026cs.CL cs.AI cs.LGcited by 0

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Abstract (arXiv)

Cloud services experience frequent incidents that require rapid diagnosis and resolution. Troubleshooting guides help engineers respond consistently, but creating them manually is labor-intensive, resulting in incomplete coverage and outdated documentation. We present FixItFlow, an automated system that generates troubleshooting guides from historical incident data using large language models. The system extracts diagnostic patterns from engineer actions, synthesizes structured guides with verified commands, and enforces strict validation to prevent fabricated content. In our evaluation with 26 engineers, generated guides achieved 61.5% positive ratings for clarity and demonstrated a 2.3x reduction in mitigation time for incidents with associated guides. These results indicate that automated guide generation can improve incident response while reducing documentation burden on engineering teams.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 18 references that are in the map. First posted on arXiv 3 May 2026.

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7 of 18 references.

References · 7

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  2. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  3. 2022Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Taskscited by 1,076
  4. 2023Automatic Root Cause Analysis via Large Language Models for Cloud Incidentscited by 84
  5. 2024Automated Root Causing of Cloud Incidents using In-Context Learning with GPT-4cited by 28
  6. 2022AutoTSG: Learning and Synthesis for Incident Troubleshootingcited by 12
  7. 2023Incident-aware Duplicate Ticket Aggregation for Cloud Systemscited by 5

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