How AI Large Models Can Automate DevOps Pipeline Failure Analysis

Introduction

As commercial banks modernize their system architectures, DevOps platforms are being built to standardize pipelines, enforce quality gates, and enable intelligent operations, greatly boosting development efficiency. However, pipeline interruptions caused by environment differences, new technology stacks, tool dependencies, and configuration errors remain frequent, often requiring time‑consuming manual investigation.

Artificial intelligence, especially large‑model AI, offers a new approach to address these challenges.

Intelligent Analysis Methodology

Applying AI large models to DevOps pipelines enables automatic collection and analysis of interruption logs, monitoring data, and code change histories. The AI system identifies root causes, categorizes failures, and suggests concrete remediation steps, reducing reliance on manual analysis.

Scenario Analysis

Traditional manual analysis involves locating interruption logs and troubleshooting based on experience. The AI‑driven approach extracts key error snippets, automatically classifies issues, and recommends handling methods for pipeline components, assisting both developers and managers.

Solution Approach

The proposed SDAF methodology follows a "Insight‑Decision‑Action‑Feedback" lifecycle, leveraging pipeline deployment scripts, policies, and logs as data sources. An AI model processes this data to automatically identify interruption types, lower analysis costs, and provide pre‑emptive warnings through similarity comparison across environments.

Implementation and Practice

Three core services were built:

Pipeline AI Model Service : A multi‑model fusion system where small models filter low‑confidence data for the large model, with expert validation before feedback to users.

Expert Knowledge Assisted Analysis : Continuous root‑cause analysis across versions builds a knowledge base of interruption patterns.

Pipeline Platform Integration : Extraction of key information from deployment scripts, policies, and logs enables comprehensive data products for downstream analysis.

Sample component interruption data (e.g., variable release component lacking namespace permissions, database component misconfiguration, file transfer component directory conflicts) illustrates how the system categorizes issues and proposes solutions.

Multi‑Model Collaborative Analysis

Small and large models cooperate, with low‑confidence outputs from the small model fed to the large model for refinement. Expert feedback further iterates the model, reducing hallucinations and improving generalization.

One‑Click Interruption Analysis

A "one‑click analysis" feature retrieves interruption logs in real time, invokes the AI service, and recommends component‑specific remediation guidance based on historical execution data.

Future Outlook

Intelligent pipeline interruption analysis is a critical research direction in DevOps, expected to become an indispensable tool for development and operations teams. Ongoing work will focus on continuous model iteration, quality‑check integration, and expanding AI knowledge bases through expert contributions and user feedback.