Worst‑Case Execution Time (WCET) in Edge Device CI/CD: Integrating RocqStat into Dev Pipelines

When latency or a missed deadline costs you a field update — integrating RocqStat WCET into CI/CD

Edge developers and embedded systems teams know the pain: a change that looks harmless in unit tests causes a worst-case timing regression on a few devices in the fleet. In 2026, with connected industrial and automotive edge systems pushing strict real-time guarantees, it's no longer enough to run functional tests in CI — you must bake timing analysis into your pipelines. This tutorial shows how to integrate RocqStat (now part of Vector's toolchain via VectorCAST) into CI/CD for embedded edge apps to enforce WCET guarantees and reduce regression risk.

Why this matters in 2026

Late 2025 and early 2026 saw a clear industry shift: timing guarantees are a first-class citizen in safety and performance validation. Vector's acquisition of StatInf's RocqStat and the planned integration into VectorCAST unifies timing analysis with functional test tooling, reflecting demand for combined verification workflows across automotive, industrial automation, and aerospace. As Vector stated after the acquisition, integrating timing safety and software testing creates a unified environment for verification — a must for safety-critical edge systems.

"Timing safety is becoming a critical ..." — Eric Barton, Vector (statement on RocqStat acquisition, Jan 2026)

That's not just vendor PR. Standards such as ISO 26262 and IEC 61508 increasingly expect evidence of timing correctness. For distributed edge systems, missing a scheduling deadline can mean missed actuations, failed safety mechanisms, or regulatory non-compliance.

What this tutorial covers (quick list)

• WCET concepts you must automate in CI for edge apps
• How RocqStat and VectorCAST fit into unit, integration, and on-target pipelines
• Concrete CI examples: GitHub Actions and Jenkins pipelines
• Practical gating rules, alerts, and regression detection strategies
• Recommendations for HIL/on-target runs, coverage, and artifacts

Key concepts — keep these in your checklist

• WCET (Worst-Case Execution Time): the maximum time a code path can take — essential for validating real-time behavior.
• Static vs measurement-based WCET: static analysis gives conservative bounds; measurement-based uses instrumentation and requires careful coverage.
• On-target execution: analysis must reflect the target CPU, caches, MMU, and scheduler behavior.
• Regression thresholds: absolute deadlines and percentage-based change detection to catch risky edits early.
• Traceability: link WCET results to source, commits, and test artifacts for audits and debugging.

High-level integration pattern

The integration has three layers. Implement them progressively:

1. Local developer checks — fast, approximate WCET estimates on workstation to prevent obvious regressions.
2. PR-level CI — run lightweight timing analysis and enforce thresholds for pull requests.
3. Nightly / Release pipelines — full RocqStat + VectorCAST runs including on-target measurement and static analysis for final verification.

Prerequisites and tooling

• RocqStat license (StatInf tech now under Vector). Coordinate with your vendor rep on CLI licensing for CI nodes.
• VectorCAST for unit/integration tests and the planned RocqStat integration (or existing plugins if already provisioned).
• Access to target hardware for on-target runs or a representative hardware-in-the-loop (HIL) lab — consider portable racks and device-farm patterns described in portable edge kit reviews.
• CI server (GitHub Actions, GitLab CI, Jenkins, or similar) with runners capable of running the tools and connecting to target hardware; many teams use containerized CI runners preconfigured for repeatability.
• Artifact repository (Nexus/Artifactory) to store timing reports and baselines.

Step 1 — Add RocqStat to local dev workflow

Start small. Developers should be able to run a fast WCET check locally before pushing changes. Use a cached, conservative model of the platform or a cross-compiled simulator if the target isn't local; see hardening and target-model advice in buyer guides like edge analytics buyer's guides.

Example: local script (illustrative)

# run-wcet-local.sh - illustrative
TARGET_MODEL="arm-cortex-a53-sim"
SRC_DIR=src
BUILD_DIR=build
mkdir -p $BUILD_DIR
# Build instrumented unit tests
make CROSS_COMPILE=aarch64-none-elf- O=build
# Run VectorCAST/unit harness (example command)
vectorcast --project my_project --run-unit-tests --output $BUILD_DIR/vcast
# Run RocqStat quick analysis (example)
rocqstat --platform $TARGET_MODEL --input $BUILD_DIR/vcast/tests.trace --mode quick --output $BUILD_DIR/wcet-report.json
# Print summary
jq '.summary' $BUILD_DIR/wcet-report.json

Note: Commands above are illustrative. Use your vendor docs for exact CLI options. The goal is a cheap, repeatable local check that catches obvious regressions.

Step 2 — PR-level CI: enforce timing gates

PRs should fail fast if a change increases WCET beyond acceptable bounds. For speed, run a delta check — compare PR results to the baseline for the branch. Typical gating policies:

• Absolute deadline breach: fail if any task WCET > assigned deadline
• Relative regression: fail if any task's WCET increases by > 5% or 1 ms (configurable)
• Whitelist exceptions: allow non-critical tasks to exceed small thresholds

GitHub Actions example

This sample runs a lightweight VectorCAST + RocqStat quick analysis and posts a PR comment. Replace placeholders with your CLI and license config.

name: PR-WCET-Check
on: [pull_request]

jobs:
  wcet:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Setup toolchain
        run: |
          sudo apt-get update && sudo apt-get install -y build-essential
          # Install VectorCAST / RocqStat CLI – vendor installer
      - name: Build
        run: make -j$(nproc)
      - name: Unit tests (VectorCAST)
        run: vectorcast --project my_project --run-unit-tests --output vcast/output
      - name: RocqStat quick WCET
        run: rocqstat --quick --input vcast/output/trace --output wcet.json
      - name: Compare with baseline
        env:
          BASELINE_URL: ${{ secrets.WCET_BASELINE_URL }}
        run: |
          curl -s $BASELINE_URL -o baseline.json
          python3 tools/compare_wcet.py baseline.json wcet.json || exit 1

In this flow, compare_wcet.py returns non-zero if thresholds are exceeded, causing the PR to fail. The script should post a detailed comment with the diff and links to affected functions.

Step 3 — Nightly and release pipelines: full analysis

Nightly jobs gather complete coverage, run static + measurement-based analysis with RocqStat, and include on-target traces where possible. These runs are slower but provide certified evidence for releases; many teams schedule full runs in the same cadence as other heavy pipelines (compare patterns in long-running model CI such as model CI/CD).

Components of a nightly run

• Full VectorCAST unit and integration test suites with coverage instrumentation
• RocqStat static analysis on compiled binaries with target model (caches, pipelines, memory layout)
• On-target execution traces collected from a HIL rack or device farm — automated via SSH, ADB, or vendor test harness; portable device-farm tooling and rack reviews are useful context: portable edge kits
• Consolidation step: merge static bounds and measured traces into a verified WCET report
• Artifact storage: store reports, graphs, and call-paths in artifact repo; link to ticketing systems for failures

Example Jenkins pipeline (simplified)

pipeline {
  agent { label 'on-target-runner' }
  stages {
    stage('Checkout') { steps { checkout scm }}
    stage('Build') { steps { sh 'make all' }}
    stage('VectorCAST Tests') { steps { sh 'vectorcast --project my_project --run-all --coverage out/vcast' }}
    stage('Collect On-target Traces') { steps {
      sh 'ssh testlab@hil "run_trace_capture --duration 300 --out /tmp/trace.bin" '
      sh 'scp testlab@hil:/tmp/trace.bin artifacts/trace.bin'
    }}
    stage('RocqStat Full WCET') { steps {
      sh 'rocqstat --platform arm-a53 --binary build/myapp.elf --trace artifacts/trace.bin --output artifacts/wcet-full.json'
    }}
    stage('Publish') { steps {
      sh 'curl -X POST -F file=@artifacts/wcet-full.json https://artifacts.example.com/upload'
    }}
  }
  post {
    failure {
      mail to: 'dev-team@example.com', subject: 'Nightly WCET failure', body: 'Check Jenkins job for details.'
    }
  }
}

Automated regression detection and triage

Automate not only failure detection but triage:

• Diff call-paths: show the new longest path and which functions were added to it.
• Annotate source files: comment inline on the PR with the functions that contributed most to WCET increases.
• Flaky detection: mark increases visible only in on-target but not in host-sim, and flag for HIL debug; device-farm telemetry and stream tooling also help here (running scalable micro-event streams at the edge).

Example triage strategy

1. If WCET > deadline, fail build and open a critical ticket.
2. If regression > 5% but < deadline, comment on PR with action items (optimize loop, reduce branch costs).
3. If regression < 5%, create a report and tag for later performance sprints.

Linking WCET to source and coverage

WCET is only useful for diagnosis when linked back to source. Use VectorCAST's coverage and RocqStat's path analysis to produce:

• Per-function WCET estimates
• Annotated source with time attribution
• Call-graph visualizations highlighting hot paths

Store these artifacts and include URLs in PR comments and release notes. This traceability is crucial for audits and safety certification; teams running distributed edge fleets often pair these reports with broader edge architecture patterns for reproducible deployments.

On-target variability and conservative margins

Edge hardware variability (different silicon steppings, cache behavior, or power modes) requires conservative margins. Two practical approaches:

• Platform families: maintain separate WCET baselines per hardware family.
• Safety margin policy: enforce a fixed safety margin (e.g., 20%) above measured WCET when validating deadlines.

RocqStat's static analysis helps create conservative bounds that include microarchitectural effects. Combine measurement-based evidence to tighten bounds where you can justify it. For power- and rack-level considerations when running on-target labs, practical field reviews such as portable power station comparisons can inform availability planning for remote labs.

Common pitfalls and how to avoid them

• Assuming host-sim timings equal target: always validate on representative hardware.
• Incomplete coverage: missing execution paths gives optimistic WCET. Use VectorCAST to maximize test coverage.
• Overly strict PR gates that slow development: use tiered enforcement (warn on small regressions, block on large ones).
• Not storing baselines or artifacts: you need historical data to spot trends and justify optimizations.

Practical tips from the field (2026 patterns)

• Combine static RocqStat analysis with targeted measurement traces from edge device farms — this hybrid approach balances safety and practicality (see examples of edge stream and device-farm patterns in edge streaming writeups and portable device reviews).
• Use containerized CI runners preconfigured with VectorCAST/RocqStat CLIs and license managers for repeatability.
• Automate artifact expiry and retention: keep release-level WCET evidence indefinitely; keep PR-level artifacts for 30 days.
• Expose WCET telemetry in dashboards (Grafana) to detect long-term performance drift across releases; pair dashboards with cache and microarch observability best practices (monitoring and observability for caches).

Example: failing fast with a simple comparator (Python)

#!/usr/bin/env python3
# compare_wcet.py - simplistic comparator
import json, sys
BASE=sys.argv[1]
NEW=sys.argv[2]
THRESHOLD_PERCENT=5.0
with open(BASE) as f: base=json.load(f)
with open(NEW) as f: new=json.load(f)
# Expect structure: {"functions": {"f1": 1.2, "f2": 0.5}}
for fn, new_time in new['functions'].items():
    base_time = base['functions'].get(fn, 0.0)
    if base_time == 0:
        print(f"New function {fn} with WCET {new_time}ms — review")
        continue
    delta = (new_time - base_time)/base_time*100.0
    if delta > THRESHOLD_PERCENT:
        print(f"REGRESSION: {fn}: {base_time}ms -> {new_time}ms (+{delta:.1f}%)")
        sys.exit(2)
print('OK: No regressions beyond threshold')

Integrate this script into PR CI as shown earlier. Enhance it to post to PRs, create tickets, and attach call-paths.

Evidence for audits and certification

For safety certifications, produce a package including:

• WCET reports (static and measured)
• Test cases and coverage evidence
• Hardware configuration and model files used by RocqStat
• Baseline comparisons and change logs linked to commits

Vector's roadmap to integrate RocqStat into VectorCAST (announced in Jan 2026) makes producing unified artifacts easier — single toolchain, single provenance model — strengthening your audit trails. For broader context on reproducible provenance and CDN/edge delivery patterns, see writeups on edge hosting and provenance.

Future predictions and advanced strategies (2026+)

• Tighter integration: expect VectorCAST + RocqStat to offer richer PR annotations, automatic root-cause suggestions, and pre-configured CI plugins by late 2026.
• ML-assisted triage: vendor tools will start using historical WCET data to prioritize investigation targets and predict risky commits.
• Edge-device crowdsourced traces: managed device-fleet operators will offer trace-as-a-service to improve measurement coverage across real-world conditions.

Actionable takeaways

• Start with a fast local RocqStat check in developers' workflows to catch early regressions.
• Enforce delta-based PR gates: block large WCET increases but warn on small changes to keep velocity.
• Run nightly full RocqStat + VectorCAST pipelines with on-target traces to create certified evidence.
• Store baselines and tie every WCET report to commit IDs and test artifacts for auditability.
• Plan hardware families and safety margins — hardware variability needs explicit policy.

Final notes: operations, cost, and ROI

Integrating WCET into CI increases pipeline complexity and compute cost (on-target labs, licensed CI nodes), but the ROI is high: fewer field regressions, reduced incident triage time, and stronger safety evidence for audits. In regulated verticals like automotive, the cost of a timing failure far exceeds the CI investment. For teams balancing cost and coverage, look to edge-hosting and low-cost CI innovations in the market to optimize runner costs.

Call to action

If you manage embedded edge projects, start by running a local RocqStat quick check on your hot branch this week. Next, add a PR-level delta check and schedule a single nightly on-target run. If you want a jumpstart, contact your VectorCAST/RocqStat vendor rep for CI integration guides and ask about prebuilt CI images and connectors for GitHub Actions or Jenkins. Implementing WCET gates today will cut your regression risk and build the timing evidence that regulators and product teams increasingly demand.

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