Automating Timing Analysis Alerts: From RocqStat to PagerDuty

Hook: your embedded devices timing alerts are noisy, late, or useless — here’s how to fix that

Teams building safety‑critical embedded systems increasingly rely on WCET and timing analysis (RocqStat, VectorCAST) to validate real‑time behavior. Yet those tools often live in isolation: results land in reports, not in ops channels. The result is slow triage, missed regressions, and production incidents. This guide shows how to automate WCET alerts into PagerDuty with CI gates and practical runbooks so embedded teams get actionable signals — not noise.

Why this matters in 2026

Late 2025 and early 2026 brought a clear industry shift: timing verification is being operationalized. Vector's January 2026 acquisition of RocqStat signals consolidation of timing analysis and test toolchains under VectorCAST, accelerating integrated WCET workflows. At the same time, software‑defined vehicles and edge devices carry more logic, increasing timing surface area and the cost of missed regressions. Teams must shift timing checks left and treat WCET regressions as first‑class incidents.

Overview: the integration pattern

At a high level we automate this flow:

1. WCET timing run (RocqStat/VectorCAST) produces machine‑readable results.
2. CI step parses results, computes margins against deadlines, and evaluates tolerance rules.
3. If a rule trips, the CI posts a structured event to PagerDuty (Events API v2) with links to artifacts, proof, and runbook steps.
4. PagerDuty creates an incident with severity, escalation policy, and a runbook that the on‑call uses for triage.
5. Observability exporters push metrics (WCET values, violations) into Prometheus/Grafana for trend analysis and dashboards.

Design decisions and tradeoffs

• Alert on regressions, not every result. Generate incidents for new violations or significant regressions to avoid noise.
• Use structured events. Attach JSON, artifacts, and direct links to the CI job and repository to speed triage.
• Rate limit and deduplicate. Use a deterministic dedup key (commit hash + function name) to avoid incident storms — architect this the same way you design rate limits for edge workloads and caches.
• Preserve safety traceability. Include references to test cases, requirements, and evidence for compliance (ISO 26262/DO‑178C workflows).

Concrete inputs: what the timing tool should emit

RocqStat and VectorCAST outputs vary, but the CI needs a compact JSON. If you control the pipeline, add a post‑processor that emits a canonical JSON like this:

{
  "commit": "a1b2c3d",
  "build_url": "https://ci.example/build/123",
  "target": "ecu‑main",
  "function_results": [
    { "name": "control_loop", "wcet_us": 2500, "deadline_us": 2000, "confidence": 0.95 },
    { "name": "sensor_filter", "wcet_us": 400, "deadline_us": 1000, "confidence": 0.99 }
  ],
  "summary": { "violations": 1, "max_regression_percent": 25 }
}

Key fields: wcet_us, deadline_us, confidence, and the CI build and commit metadata.

CI integration examples

Below are minimal CI patterns. The CI job parses the JSON, decides on alerting, and posts to PagerDuty.

GitHub Actions example

name: timing‑analysis
on: [push]

jobs:
  wcet-check:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Run RocqStat
        run: |
          # run tool and emit report.json
          ./run_roqcstat.sh --output report.json
      - name: Parse and alert
        env:
          PAGERDUTY_ROUTING_KEY: ${{ secrets.PAGERDUTY_ROUTING_KEY }}
        run: |
          python ci/parse_timing.py report.json --build-url ${{ github.run_url }} --commit ${{ github.sha }}

The parse_timing.py script implements the rules shown below. If it finds a violation, it posts to PagerDuty Events API v2 for reliable incident creation.

Rule examples (practical)

• Trigger an incident if wcet_us > deadline_us.
• Trigger if wcet_us increased by > 10% compared to previous main branch baseline for the same commit target.
• Ignore low confidence results (confidence < 0.9) unless the increase is > 20%.

Posting to PagerDuty: a pragmatic payload

Use PagerDuty Events API v2 to create reliable incidents. Key elements:

• routing_key: your integration key
• event_action: trigger
• dedup_key: deterministic key to deduplicate incidents (store dedup rules next to your CI configs)
• payload.summary: concise, includes component, function, and type
• payload.severity: map WCET severity to critical/major/warn
• links/attachments: include CI build URL, report JSON, and failing test case

{
  "routing_key": "YOUR_ROUTING_KEY",
  "event_action": "trigger",
  "dedup_key": "a1b2c3d_control_loop",
  "payload": {
    "summary": "WCET violation: control_loop on ecu-main (2500us > 2000us)",
    "severity": "critical",
    "source": "ci.example/timing",
    "custom_details": {
      "commit": "a1b2c3d",
      "build_url": "https://ci.example/build/123",
      "wcet_us": 2500,
      "deadline_us": 2000,
      "confidence": 0.95
    }
  },
  "links": [ { "href": "https://ci.example/build/123", "text": "CI build" }, { "href": "https://repo.example/commit/a1b2c3d", "text": "Commit" } ]
}

Security note: store routing keys in your CI secret store, rotate them periodically, and restrict network egress from CI where possible.

Runbook design: make alerts actionable

An incident without a runbook wastes time. Include a short, structured runbook in the PagerDuty incident notes or as a linked document. Use this template:

WCET Alert Runbook Template

• Title: WCET violation — {function} on {target}
• Initial Triage (1–3 min): confirm build and commit, validate the reported WCET against artifact, check confidence level.
• Reproduce (5–15 min): re-run the RocqStat/VectorCAST job locally or in a reproducible CI stage using the same artifact.
• Quick Mitigation: revert the PR or cherry‑pick a previous commit if the change caused the regression and no hotfix exists.
• Root Cause Steps: identify changed files, inspect control paths, use static timing hot spots to narrow down loops or blocking calls.
• Evidence to attach: failing report JSON, artifact, relevant trace logs, failing test case id.
• Owner & SLAs: on‑call team, expected triage within 15 min, resolution or mitigation plan in 2 hours.

Embed links to automated reproducibility: the PagerDuty incident should include a one‑click link to re‑run the timing job in CI with the same parameters.

Observability: metrics and dashboards

Treat WCET like any other metric. Export metrics from your CI or timing post‑processor to Prometheus:

# Metrics to export
wcet_latest_microseconds{function="control_loop",target="ecu-main"} 2500
wcet_deadline_microseconds{function="control_loop",target="ecu-main"} 2000
wcet_violations_total{function="control_loop",target="ecu-main"} 3
wcet_regression_percent{function="control_loop",target="ecu-main"} 25.0

Create Grafana dashboards for:

• WCET time series per function
• Violation count and regression percent
• Heatmap of WCET across targets and commits

Use alerting rules in your observability stack as a secondary safety net, but keep PagerDuty as the primary incident hub.

Advanced strategies for low noise and high signal

1) Baseline comparisons

Store main‑branch baselines for each function and target. Alert only when the regression crosses absolute deadline or relative percent thresholds.

2) Canary and staged rollouts

If you deploy firmware or runtime components, gate releases by timing CI checks and implement canary thresholds in the field. A canary device running a representative workload can feed real execution times back to the observability system.

3) Dynamic severity mapping

Map severity based on safety impact: if function is on a safety chain (brake control) escalate to critical; if it's a monitoring function mark as warning. This mapping should be codified in your CI configuration.

4) Auto‑annotated PRs

When a PR causes a regression, have CI post a comment with the failing functions, the diff of timing, and a link to the runbook. This reduces back‑and‑forth.

Example: a minimal Python parser that decides and posts

import json
import os
import requests

PD_KEY = os.environ['PAGERDUTY_ROUTING_KEY']

with open('report.json') as f:
    r = json.load(f)

violations = []
for fn in r['function_results']:
    if fn['wcet_us'] > fn['deadline_us']:
        violations.append(fn)

if not violations:
    print('No violations')
    exit(0)

# Build payload for PagerDuty
fn = violations[0]
dedup = f"{r['commit']}_{fn['name']}"
payload = {
  'routing_key': PD_KEY,
  'event_action': 'trigger',
  'dedup_key': dedup,
  'payload': {
    'summary': f"WCET violation: {fn['name']} ({fn['wcet_us']}us > {fn['deadline_us']}us)",
    'severity': 'critical',
    'source': 'ci.timing',
    'custom_details': { 'report': r }
  }
}
requests.post('https://events.pagerduty.com/v2/enqueue', json=payload)

Operational considerations & security

• Secrets management: use CI secret stores and restrict who can edit the CI pipeline.
• Rate limits: PagerDuty and your CI may apply limits; batch or throttle alerts when many functions spike together (design this like an edge workload).
• Traceability: keep artifacts and reports linked for compliance audits (ISO 26262 traceability requirements increased in 2025).
• Rollback policy: connect PagerDuty incidents to automated rollback or block merges if the risk is high and the fix is urgent.

Case study: embedded team pilot (conceptual)

Context: 6‑engineer embedded team running VectorCAST and a RocqStat integration. They saw frequent timing regressions late in the release cycle and long triage times.

What they did:

1. Added a RocqStat post‑processor to emit canonical JSON and Prometheus metrics.
2. Added a CI job to run timing checks on PRs and main branch nightly baselines.
3. Integrated the parser to send structured events to PagerDuty with dedup keys and runbook links.
4. Created Grafana dashboards and an observability alert that opens a warning ticket (non‑PD) for trending regressions.

Result: first response time to timing incidents dropped from hours to 12 minutes, noise dropped by 70% because only meaningful regressions triggered incidents, and developers could attach a short remediation PR to incidents directly from PagerDuty notes.

2026 predictions: what to expect next

Integration between timing tools and verification suites will deepen after Vector's acquisition of RocqStat. Expect these trends in 2026:

• More toolchains emitting machine‑readable timing results by default.
• Bundled integrations between timing tools and incident platforms (PagerDuty) via marketplace extensions or official connectors.
• Greater emphasis on continuous timing verification as a compliance artifact for ISO 26262/DO‑178C workflows.
• Automated remediation patterns where low‑risk regressions trigger rollout blocks and developer notifications automatically.

Checklist: get from manual reports to actionable PagerDuty incidents

1. Ensure your timing tool can export JSON; add a post‑processor if not.
2. Define alert rules: absolute deadline, relative regression percent, confidence thresholds.
3. Implement CI parsing step with secrets for PagerDuty; use deterministic dedup keys.
4. Create a concise runbook template and attach it to PagerDuty incidents.
5. Export metrics to Prometheus/Grafana for dashboards and trend alerts.
6. Iterate thresholds after two sprints to reduce false positives.

Common pitfalls and how to avoid them

• Too many low‑confidence alerts: filter or down‑weight them in CI.
• Incidents without context: always attach failing report JSON, commit, and runbook link.
• No deduplication: use dedup_key based on commit+function+target to prevent floods — see our CI deduplication patterns.
• Secrets in plaintext: never embed routing keys in logs or repo files.

Make timing alerts part of your delivery pipeline, not an afterthought. The faster you close the loop between verification and ops, the lower your run‑time risk.

Actionable takeaways

• Start small: automate one critical function’s WCET checks into PagerDuty — prove value, then expand.
• Use structured events: include commit, build URL, and report JSON for fast triage.
• Codify runbooks: put triage steps, reproduce commands, and rollback options directly in the incident.
• Monitor trends: send time series to Prometheus and use Grafana to catch slowly worsening regressions.

Next steps and call to action

If you operate embedded systems and want to pilot this pattern, start with a 2‑week spike: add a RocqStat/VectorCAST post‑processor, a CI parse step, and a PagerDuty integration for one target function. Need ready‑made scripts, CI templates, and a runbook bundle tuned for VectorCAST and RocqStat? Contact the simplistic.cloud Integrations team for a pilot package that includes GitHub Actions, Jenkins pipelines, PagerDuty mappings, and Grafana dashboards.

Get help building the pipeline and runbooks so your team sees meaningful timing alerts — not noise.

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