AI-Powered DevOps

AI in the Software Delivery Pipeline

Code Review Automation

python
import openai
def ai_code_review(diff: str, context: dict) -> dict:
    client = openai.OpenAI()
    
    prompt = f"""Review this code change:
    
    Repository: {context['repo']}
    PR Title: {context['title']}
    
    Diff:
    {diff[:8000]}
    
    Check for:
    1. Security vulnerabilities (OWASP Top 10)
    2. Performance issues
    3. Code style violations
    4. Missing tests
    5. Documentation gaps
    6. Breaking changes
    
    Return JSON with issues and severity (info/warning/error)."""
    
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": prompt}],
        response_format={"type": "json_object"}
    )
    return json.loads(response.choices[0].message.content)

Deployment Risk Prediction

python
def predict_deployment_risk(pr_metadata: dict) -> float:
    features = {
        "lines_changed": pr_metadata["lines_changed"],
        "files_changed": pr_metadata["files_changed"],
        "test_coverage_delta": pr_metadata["coverage_delta"],
        "pr_age_days": pr_metadata["days_open"],
        "author_experience": pr_metadata["author_merge_count"],
        "has_migration": pr_metadata["has_db_migration"],
        "affects_critical_path": pr_metadata["touches_payment"]
    }
    return risk_model.predict_proba([list(features.values())])[0][1]

Anomaly Detection for Incidents

python
from sklearn.ensemble import IsolationForest
import numpy as np
Train on normal operation metrics
normal_metrics = load_historical_metrics(days=30)
detector = IsolationForest(contamination=0.01, random_state=42)
detector.fit(normal_metrics)
def detect_anomaly(current_metrics: np.array) -> bool:
    score = detector.score_samples([current_metrics])[0]
    return score < -0.5  # Anomaly threshold
Real-time monitoring
for metrics_snapshot in metrics_stream:
    if detect_anomaly(metrics_snapshot):
        trigger_alert(metrics_snapshot)

Automated Root Cause Analysis

python
def analyze_incident(logs: str, metrics: dict, recent_deploys: list) -> str:
    prompt = f"""Analyze this production incident:
    
    Error logs: {logs[-3000:]}
    Key metrics: {json.dumps(metrics)}
    Recent deployments: {json.dumps(recent_deploys)}
    
    Provide:
    1. Most likely root cause
    2. Contributing factors
    3. Immediate remediation steps
    4. Prevention recommendations"""
    
    return call_llm(prompt)

GitHub Actions AI Integration

yaml
name: AI-Assisted PR Review
on:
  pull_request:
    types: [opened, synchronize]
jobs:
  ai-review:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: AI Code Review
        run: |
          python scripts/ai_review.py             --pr-number=${{ github.event.number }}             --openai-key=${{ secrets.OPENAI_API_KEY }}