AIOps

BigPanda AIOps: Correlating Test Failures with Production Incidents

HelpMeTest

HelpMeTest

— 6 min read
BigPanda AIOps: Correlating Test Failures with Production Incidents

Most incident management tools answer the question "who should we page?" BigPanda is built to answer a harder question: "what is actually causing this?"

BigPanda is an AIOps platform that ingests alerts from across your stack — monitoring tools, CI/CD pipelines, testing infrastructure, cloud providers, application performance tools — and uses machine learning to correlate them into unified incidents. Instead of 40 separate alerts firing when a database goes down, you get one incident with all the related alerts grouped together and a probable root cause surfaced automatically.

For QA teams, this changes how test failures fit into the broader incident picture. When your integration test suite fails at the same time your monitoring tool reports elevated API latency, BigPanda can recognize these as symptoms of the same underlying issue rather than two separate problems.

The Problem BigPanda Solves

Modern infrastructure generates a lot of alerts. A single production incident can trigger:

  • CPU alerts from cloud monitoring
  • API error rate alerts from APM
  • Latency alerts from synthetic monitoring
  • Failed health checks from load balancers
  • Test suite failures from CI/CD
  • Database connection pool alerts
  • Queue depth alerts

Each monitoring tool treats these as independent alerts. Your on-call engineer gets paged 8 times about the same incident and has to manually figure out what's related.

BigPanda ingests all of these alerts and applies ML-based correlation to group them into a single incident: "Database connectivity issue affecting payment service, API gateway, integration tests, and synthetic monitoring — root cause: RDS failover in us-east-1."

That's the difference between your engineer spending 45 minutes correlating alerts and spending 45 minutes fixing the database.

How BigPanda Works

Data Ingestion — BigPanda collects alerts from your tools via integrations (native connectors or webhook). It can ingest alerts from 300+ tools including Datadog, New Relic, CloudWatch, Dynatrace, and custom applications.

Correlation — BigPanda's Open Box AI analyzes incoming alerts in real time. It groups alerts based on:

  • Timing proximity (alerts that fire within the same window)
  • Topology relationships (alerts from services that depend on each other)
  • Historical co-occurrence (alerts that have appeared together in past incidents)
  • Tags and metadata (alerts about the same region, service, or environment)

Incidents — Correlated alerts are grouped into a BigPanda incident. The incident shows all contributing alerts, a probability-ranked list of root cause candidates, and a severity score.

Enrichment — BigPanda pulls context from your CMDB, Jira, and other tools to add relevant information to each incident: service owner, related changes, affected customers.

Response — From BigPanda, you can create ITSM tickets (ServiceNow, Jira), page on-call teams (PagerDuty, OpsGenie), or trigger automated remediation.

Connecting Your Testing Infrastructure

Inbound Integration via Webhook

BigPanda accepts alerts via its Events API. Send test failures to BigPanda and it correlates them with alerts from your other tools.

API endpoint: https://integrations.bigpanda.io/services/alerts/v2.0/alerts

Authentication: BigPanda provides an app key and bearer token for each integration.

Sending Test Failures to BigPanda

curl -X POST https://integrations.bigpanda.io/services/alerts/v2.0/alerts \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $BIGPANDA_BEARER_TOKEN" \
  -d '{
    "app_key": "$BIGPANDA_APP_KEY",
    "alerts": [{
      "status": "critical",
      "host": "ci-pipeline",
      "check": "integration-test-suite",
      "description": "Integration tests failed: 14 failures in payment-service tests",
      "cluster": "ci-staging",
      "service": "payment-service",
      "environment": "staging",
      "branch": "main",
      "run_url": "https://github.com/org/repo/actions/runs/12345"
    }]
  }'

When tests pass, send a resolution:

curl -X POST https://integrations.bigpanda.io/services/alerts/v2.0/alerts \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $BIGPANDA_BEARER_TOKEN" \
  -d '{
    "app_key": "$BIGPANDA_APP_KEY",
    "alerts": [{
      "status": "ok",
      "host": "ci-pipeline",
      "check": "integration-test-suite",
      "description": "Integration tests passing"
    }]
  }'

BigPanda tracks the host + check combination as the identity of this alert stream. Use consistent values so BigPanda can track state changes over time.

GitHub Actions Integration

jobs:
  test-and-report:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      
      - name: Run integration tests
        id: integration_tests
        run: npm run test:integration 2>&1 | tee /tmp/test-output.txt
        continue-on-error: true
      
      - name: Report to BigPanda
        if: always()
        run: |
          STATUS=$([[ "${{ steps.integration_tests.outcome }}" == "success" ]] && echo "ok" || echo "critical")
          DESCRIPTION=$([[ "$STATUS" == "ok" ]] && echo "Integration tests passing" || echo "Integration tests failed on ${{ github.ref_name }}")
          
          curl -X POST https://integrations.bigpanda.io/services/alerts/v2.0/alerts \
            -H "Content-Type: application/json" \
            -H "Authorization: Bearer ${{ secrets.BIGPANDA_BEARER_TOKEN }}" \
            -d "{
              \"app_key\": \"${{ secrets.BIGPANDA_APP_KEY }}\",
              \"alerts\": [{
                \"status\": \"$STATUS\",
                \"host\": \"github-actions\",
                \"check\": \"integration-tests-${{ github.repository }}\",
                \"description\": \"$DESCRIPTION\",
                \"service\": \"${{ github.repository }}\",
                \"environment\": \"staging\",
                \"branch\": \"${{ github.ref_name }}\",
                \"commit\": \"${{ github.sha }}\"
              }]
            }"

Correlation in Practice

Here's where BigPanda becomes genuinely useful for QA teams.

Scenario: Your integration test suite fails at 14:37. Simultaneously, your APM tool reports elevated API latency on the payment service, and your synthetic monitoring shows checkout flow degraded.

Without BigPanda:

  • Your CI system pages the on-call developer about test failures
  • Your APM tool pages the platform engineer about latency
  • Your monitoring tool pages the SRE about synthetic monitoring

Three pages, three people investigating the same underlying issue separately.

With BigPanda:

  • All three alerts fire within a 90-second window
  • BigPanda correlates them into one incident: "Payment service degradation"
  • One page goes to the incident commander
  • The incident view shows all three alert sources, the timeline, and the probable root cause
  • BigPanda identifies that a deployment happened at 14:34 and surfaces it as the likely cause

One page. One person investigating. Context already assembled.

Root Cause Analysis with BigPanda

BigPanda's AI identifies root cause candidates by analyzing:

Alert ordering — Which alert fired first? The upstream alert is often the root cause.

Topology — Which services depend on which? If the database alert fires, all dependent services will follow.

Change correlation — BigPanda ingests deployment events and correlates them with incident timing. A deployment that happened 3 minutes before 8 correlated alerts is a strong root cause candidate.

Historical patterns — Has this alert pattern occurred before? What was the root cause last time?

BigPanda presents root cause candidates ranked by probability. This isn't a black box — it shows you why each candidate was suggested.

Ingesting Deployment Events

To enable change correlation, send deployment events to BigPanda:

# Send deployment event when deploying to staging/production
curl -X POST https://integrations.bigpanda.io/services/alerts/v2.0/alerts \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $BIGPANDA_BEARER_TOKEN" \
  -d '{
    "app_key": "$BIGPANDA_APP_KEY",
    "alerts": [{
      "status": "ok",
      "host": "deployment",
      "check": "payment-service-deploy",
      "description": "payment-service v2.4.1 deployed to staging",
      "service": "payment-service",
      "environment": "staging",
      "version": "v2.4.1",
      "deployed_by": "github-actions"
    }]
  }'

When this deployment event is followed by test failures, BigPanda surfaces the deployment as a probable root cause.

Integration with ITSM and On-Call Tools

BigPanda doesn't replace your on-call management tool — it feeds into it with higher-quality, pre-correlated incidents.

PagerDuty integration — BigPanda creates PagerDuty incidents from its correlated alert groups. One PagerDuty incident per BigPanda incident, not one per alert.

OpsGenie integration — Same pattern. BigPanda creates one OpsGenie alert per correlated incident.

ServiceNow — BigPanda creates ITSM tickets automatically, pre-populated with all correlated alerts and the root cause analysis.

Jira — Create Jira issues from BigPanda incidents for bug tracking and post-mortem follow-up.

The workflow: BigPanda correlates → creates one high-quality incident → sends to PagerDuty → pages on-call → engineer sees correlated context from the start.

HelpMeTest + BigPanda

HelpMeTest runs continuous end-to-end tests against your production environment. These tests are a leading indicator — they often detect production problems before your users report them.

Integrating HelpMeTest with BigPanda makes these test results part of your correlation picture.

Configure HelpMeTest to send test results to BigPanda via webhook:

On test failure:

{
  "app_key": "YOUR_APP_KEY",
  "alerts": [{
    "status": "critical",
    "host": "helpmetest",
    "check": "{{test_name}}",
    "description": "Production test failed: {{error_message}}",
    "service": "{{service_name}}",
    "environment": "production",
    "test_url": "{{test_url}}"
  }]
}

On test recovery:

{
  "app_key": "YOUR_APP_KEY",
  "alerts": [{
    "status": "ok",
    "host": "helpmetest",
    "check": "{{test_name}}",
    "description": "Production test recovered"
  }]
}

When HelpMeTest detects a checkout flow failure at the same time your APM shows payment service errors, BigPanda correlates them. Your on-call engineer sees one incident with both signals — and knows immediately that customers are likely experiencing checkout failures, not just that an internal health check failed.

Noise Reduction Metrics

BigPanda's primary value proposition is alert noise reduction. For QA and operations teams, this typically looks like:

  • Alert reduction — BigPanda customers typically see 95%+ reduction in actionable alert volume (many alerts → few incidents)
  • MTTR improvement — Pre-correlated incidents with root cause analysis reduce diagnosis time
  • False positive reduction — Correlated incidents are more likely to represent real problems than individual alerts

Track these metrics after implementing BigPanda by comparing your pre- and post-implementation PagerDuty/OpsGenie alert volume and incident duration.

When BigPanda Makes Sense

BigPanda is an enterprise-tier tool with enterprise pricing (typically $15-30+/user/month, custom contracts). It makes sense when:

  • You have multiple monitoring tools generating overlapping alerts
  • Alert noise is a significant problem (engineers ignoring pages)
  • You're running complex microservices where incidents cascade across services
  • Your team is spending significant time on alert correlation rather than incident resolution

It's overkill when:

  • You have a simple stack with one or two monitoring tools
  • Alert volume is manageable
  • You're a small team where manual correlation is fast enough

For QA teams embedded in larger engineering organizations with mature observability stacks, BigPanda provides genuine value by making test failures visible in the broader incident context rather than isolated QA noise.

Getting Started

  1. Request a BigPanda demo or trial at bigpanda.io
  2. Start with your highest-volume alert source (likely your APM or infrastructure monitoring)
  3. Add CI/CD pipeline alerts as a second source
  4. Add testing infrastructure (HelpMeTest, Selenium Grid, etc.) as a third source
  5. Observe how BigPanda correlates alerts across sources
  6. Tune correlation rules based on false positives you observe
  7. Connect to your on-call tool (PagerDuty/OpsGenie) for paging
  8. Connect to ServiceNow or Jira for ITSM workflow

Start with a 30-day proof of concept focused on a single high-traffic service. Measure alert volume before and after. If the correlation quality is good and the noise reduction is real, expand from there.

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