6.8Automating Dashboard Data Collection

Data Sources and Integration

Data Source	What It Provides	Integration Method
CI/CD pipeline (GitHub Actions, GitLab CI, Jenkins)	Test results, build status, deployment events	Webhook / API push to dashboard data store
Test frameworks (JUnit, pytest, Playwright)	Pass/fail results, execution time, screenshots	JUnit XML / JSON export, parsed by pipeline
Bug tracker (JIRA, Linear, GitHub Issues)	Bug counts, severity, status, cycle time	API polling or webhook on status change
Code coverage tools (Istanbul, JaCoCo)	Line/branch/function coverage	Coverage report in CI, parsed and stored
APM / monitoring (Datadog, New Relic)	Production error rates, latency, availability	Direct dashboard integration
Custom scripts	Flaky test detection, test categorization	Scheduled jobs that push data to the dashboard

Example: Automated Pipeline to Grafana

# GitHub Actions step that pushes test metrics to Grafana
- name: Push test metrics
  run: |
    TOTAL=$(cat test-results.json | jq '.total')
    PASSED=$(cat test-results.json | jq '.passed')
    FAILED=$(cat test-results.json | jq '.failed')
    DURATION=$(cat test-results.json | jq '.duration')

    curl -X POST "$GRAFANA_PUSH_URL" \
      -H "Content-Type: application/json" \
      -d "{
        \"test_total\": $TOTAL,
        \"test_passed\": $PASSED,
        \"test_failed\": $FAILED,
        \"test_duration_seconds\": $DURATION,
        \"branch\": \"$GITHUB_REF\",
        \"commit\": \"$GITHUB_SHA\",
        \"timestamp\": \"$(date -u +%Y-%m-%dT%H:%M:%SZ)\"
      }"

Keeping Dashboards Current Without Manual Effort

1. Automate data collection at every pipeline run (test results, coverage, build status)
2. Schedule periodic data pulls for metrics that change less frequently (bug backlog, customer-reported defects)
3. Set up alerts for metrics that cross thresholds (flaky rate > 5%, coverage drops below 70%)
4. Review dashboard relevance quarterly -- remove metrics nobody looks at, add metrics people are asking for