Practical Guide: Integrating Timing Analysis into CI for Safety-Critical Systems

Hook: Why timing belongs in your CI now

If you build embedded, safety-critical software you already know that a single timing regression can cost a certification cycle or — worse — a safety incident. Yet many teams run unit tests and static analysis in CI/CD pipelines and treat WCET timing analysis like a separate, manual step. That gap creates risk and slows delivery.

This guide shows a practical, hands-on way to add WCET timing analysis into CI so that timing regressions are detected continuously, not after the fact. We use concepts proven in tools like VectorCAST and the recently acquired RocqStat technology (Vector's January 2026 acquisition), and deliver concrete CI examples for GitHub Actions, GitLab CI and Jenkins.

The context in 2026: timing analysis moves into CI

By 2026 the industry trend is clear: vendors are unifying static and dynamic verification with timing analysis. Vector's acquisition of RocqStat (announced January 2026) is a signpost — the industry expects WCET to be part of the standard developer workflow rather than a specialist activity. Teams are adopting continuous verification approaches that treat safety constraints (including timing) as first-class gates in CI/CD pipelines.

"Timing safety is becoming a critical requirement for modern embedded systems" — Vector press coverage, Jan 2026

What you gain by integrating WCET into CI

• Early detection of timing regressions—avoid late-cycle rework.
• Traceable baselines and artifacted reports to support audits (ISO 26262 / DO-178C context).
• Automated gating so PRs that increase worst-case latency fail fast.
• Reproducibility when you run timing experiments in containerized or ephemeral environments or hardware-in-the-loop (HIL) setups.

High-level approach (three pillars)

Integrating WCET into CI requires attention to three pillars. Implement all three to get reliable, meaningful results.

Pillar 1 — Deterministic execution environment

• Pin compiler toolchains, linker scripts, and SDK versions in CI images.
• Use containers (Docker) or dedicated build agents with CPU frequency governors and hyper-threading controlled.
• Document hardware profiles (CPU, caches, clocks) used to generate timing baselines. For low-cost HIL you can use boards like a Raspberry Pi-class runner as a reproducible target for early validation.

Pillar 2 — Two-pronged timing analysis

Use static WCET analysis (path-sensitive, worst-case bounding) plus measurement-based runs to validate assumptions. Static tools like RocqStat-style analyzers provide safe bounds; measurement catches pathological platform issues and gives evidence for tuning. Emulator-based measurement and QEMU checks benefit from performance tuning described in embedded performance playbooks (see embedded Linux optimization notes).

Pillar 3 — Continuous verification and gating

• Run timing checks on PRs and mainline builds.
• Compare results to a stored baseline and fail builds on regression beyond a threshold.
• Store artifacts and reports for traceability and audits. Integrate results into an observability backend (Prometheus/InfluxDB) and leverage edge observability patterns for low-latency metrics collection.

Preparation: what you need before adding WCET to CI

1. Reproducible build: CI must produce identical binaries each run (deterministic flags, identical timestamps masked).
2. Test harness: a runner harness that exercises target functions or tasks with deterministic inputs.
3. Timing toolchain access: CLI or API access to your WCET tool (e.g., RocqStat concepts, VectorCAST timing integrations).
4. Hardware or cycle-accurate emulator: HW-in-the-loop (recommended for final verification) or QEMU/SWF models for fast CI runs.
5. Baseline dataset: a stored set of WCET numbers and thresholds per measured function/module.

Example CI workflow (overview)

At a high level a PR or commit triggers the following pipeline stages:

1. Checkout & reproducible build.
2. Unit tests / static checks (fast checks remain the same).
3. Instrumentation or analysis pass that prepares an input for the WCET tool.
4. Run static WCET analysis (path analysis) and measurement runs on emulator or HIL.
5. Compare results against baseline; create report and status for the PR.
6. Archive artifacts and metrics for trends and audits.

Concrete example: GitHub Actions job

Below is a minimal, practical workflow example you can adapt. It shows both a fast emulator-based check and an optional HIL stage that you may run nightly or on mainline only.

# .github/workflows/wcet-check.yml
name: WCET Continuous Verification

on:
  pull_request:
    branches: [ main ]
  workflow_dispatch: {}

jobs:
  build-and-analyze:
    runs-on: ubuntu-22.04
    steps:
      - uses: actions/checkout@v4

      - name: Setup toolchain
        run: |
          sudo apt-get update
          sudo apt-get install -y build-essential llvm-14
          # install your vendor CLI or download docker image
          docker pull mycompany/wcet-tool:latest

      - name: Reproducible build
        run: |
          export SOURCE_DATE_EPOCH=123456789
          make clean && make all

      - name: Prepare WCET inputs
        run: |
          ./scripts/generate_cfg_for_wcet.sh build/output.elf build/cfg.json

      - name: Run static WCET analysis (emulator)
        run: |
          docker run --rm -v ${{ github.workspace }}:/ws mycompany/wcet-tool:latest \
            /opt/wcet/bin/rocqstat analyze --binary /ws/build/output.elf --cfg /ws/build/cfg.json --out /ws/wcet/result.json

      - name: Run measurement-based experiment (QEMU)
        run: |
          docker run --rm -v ${{ github.workspace }}:/ws mycompany/wcet-tool:latest \
            /opt/wcet/bin/measure-run --emulator qemu-system-arm -b /ws/build/output.elf --trace /ws/wcet/trace.log

      - name: Compare to baseline
        run: |
          python3 tools/compare_wcet.py --current wcet/result.json --baseline artifacts/wcet-baseline.json --threshold 0.05

      - name: Upload artifacts
        uses: actions/upload-artifact@v4
        with:
          name: wcet-report-${{ github.sha }}
          path: wcet/

Notes on the GitHub Actions example

• Use a pinned container image for the toolchain to ensure reproducibility. For ephemeral CI images and sandboxing patterns see ephemeral workspaces.
• Set SOURCE_DATE_EPOCH and other env vars to remove build timestamp variability.
• Split static analysis (fast) from HIL runs (slow). Consider making HIL runs run-once-nightly instead of per-PR.

GitLab CI and Jenkins examples (short)

GitLab CI snippet

stages:
  - build
  - wcet

build:
  image: registry.example.com/toolchain:stable
  script:
    - make clean && make all
  artifacts:
    paths:
      - build/output.elf

wcet_static:
  image: registry.example.com/wcet-tool:latest
  stage: wcet
  script:
    - /opt/wcet/bin/rocqstat analyze --binary build/output.elf --cfg build/cfg.json --out wcet/result.json
  artifacts:
    paths:
      - wcet/result.json

Jenkins (Declarative Pipeline stage)

pipeline {
  agent any
  stages {
    stage('Build') {
      steps { sh 'make all' }
    }
    stage('WCET Static') {
      steps {
        sh 'docker run --rm -v $PWD:/ws mycompany/wcet-tool /opt/wcet/bin/rocqstat analyze --binary /ws/build/output.elf --cfg /ws/build/cfg.json --out /ws/wcet/result.json'
        archiveArtifacts artifacts: 'wcet/result.json', onlyIfSuccessful: true
      }
    }
  }
}

Designing robust comparisons and baselines

A naive comparison (current WCET > baseline WCET) will flood you with false positives. Follow these pragmatic rules:

• Use percent-based thresholds for relative sensitivity (e.g., 3–5% for critical loops, 10% for unconstrained modules).
• Separate static and measured thresholds — static WCET should be a safe upper bound; measurement-based results should remain under a lower, operational threshold.
• Track trends across time using a metrics backend (Prometheus, InfluxDB); alert on slope changes, not only single-run deltas.
• Classify changes into acceptable, review-required, and fail-build categories, and automate labeling of PRs accordingly.

Automated triage and developer feedback loop

Integrate WCET results with PR comments and code owners. Provide the following in feedback:

• What function(s) regressed (file:line, call chain snapshot).
• Delta in percentage and absolute microseconds/milliseconds.
• Suggested quick checks (e.g., avoid recursion, review introduced loops, check compiler flags).

Example: a bot posts a PR comment with a table linking to the WCET artifact and a pre-signed HIL run request if deeper investigation is required. Consider integrating these reports with lightweight IDE tooling and developer workflows (see reviews of modern tooling like Nebula IDE for ideas on developer feedback loops).

Making measurement runs reliable

Measurement-based runs face environmental noise. Follow these tips to make runs CI-friendly:

• Isolate the CPU or use dedicated runners for measurements.
• Disable dynamic frequency scaling and turbo modes on measurement agents.
• Run multiple trials and use statistical metrics (median, p99) rather than raw min/max.
• Use deterministic inputs and reset state between runs (hardware reset or warm/cold start strategies). Embrace sandboxing and strict isolation practices described in modern desktop-agent security guidance (sandboxing & isolation best practices).

Hybrid workflow: fast checks + slow authoritative checks

For developer velocity, split timing verification into two lanes:

• Fast lane: Static WCET + emulator measurement run on PRs (minutes).
• Authoritative lane: Full static analysis + HIL runs for mainline or nightly (hours, gated, archived). Use a small fleet of reproducible HIL targets—document hardware profiles and keep artifacts tied to their hardware images (see embedded performance and HIL guidance at embedded Linux performance notes).

The authoritative lane can be used to update baselines when changes are accepted, and to generate audit-ready reports for certification.

Evidence & reporting for audits

Safety processes require traceable evidence. Automate the production of:

• Timestamped WCET reports with tool and dataset versions.
• Signed artifacts (hashes) and links to the exact binaries tested.
• Change logs and PR references that explain why thresholds were updated.

Advanced strategies and 2026 trends to watch

Look beyond the basics with these strategies that are gaining traction in 2026.

• Integrated toolchains: Vendors like Vector are merging WCET and code testing workflows, enabling a single project file for tests and timing analysis.
• ML-assisted regression triage: Use lightweight ML to classify whether a timing regression is likely code-related or environment-related based on trace fingerprints.
• Cloud-HIL federation: On-demand HIL fleets allow teams to run hardware-accurate tests in cloud-hosted labs triggered from CI.
• Probabilistic WCET: Statistical WCET models (pWCET) provide operationally useful bounds when deterministic bounds are overly conservative.

Case study: Bringing it together at a mid-sized embedded team (fictionalized)

Team Alpha builds a safety-critical motor controller. Before 2026 they only ran timing checks manually before major releases. After adopting a CI-first approach:

1. They containerized a fixed GCC/SDK stack and used reproducible flags in CI.
2. They added a static WCET job using a rocqstat-style CLI in PR pipelines for fast feedback (under 15 minutes).
3. They scheduled nightly HIL runs from the authoritative lane and kept a baseline artifact bucket with signed reports.
4. They created a bot to post PR comments with concise deltas and links to artifacts; small performance changes were triaged by code owners.

Results: fewer last-minute fixes, shorter certification evidence collection cycles, and faster developer feedback.

Practical checklist to implement this in your project

1. Pin and containerize your toolchain; make builds deterministic.
2. Create a minimal measurement harness that can be exercised by emulator and HIL.
3. Automate a static-WCET analysis stage and a fast measurement stage in PR pipelines.
4. Store baselines and implement a compare-and-threshold script with clear categories.
5. Archive signed artifacts and link them from PRs for traceability.
6. Plan authoritative HIL runs (nightly/mainline) to update baselines and produce audit reports.
7. Instrument alerting and trend dashboards for long-term drift detection.

Common pitfalls and how to avoid them

• Pitfall: Unstable CI runners produce noisy measurements. Fix: dedicate runners or use controlled containers and run multiple trials.
• Pitfall: Too strict thresholds cause developer fatigue. Fix: tier thresholds and require human review for marginal deltas.
• Pitfall: Missing linkage between code and report. Fix: always publish the tested binary hash and PR/commit id with the report.

Tooling: how RocqStat concepts and VectorCAST fit

Vendors are integrating timing analysis into broader verification ecosystems. The RocqStat approach emphasizes precise path-sensitive static WCET estimation, while VectorCAST focuses on code testing & integration. Combining these concepts gives teams:

• Unified project metadata (source, build, test, timing) for reproducible runs.
• Shared telemetry to pinpoint code paths responsible for worst-case scenarios.
• Better traceability from failing test or timing regression to source change and test case.

Expect vendor integrations in 2026 to provide APIs and CI-friendly CLIs that can be embedded into the patterns shown above. See also practical notes on IDE and developer feedback integration in tooling reviews like Nebula IDE.

Actionable takeaways

• Start small: Add static WCET runs in PRs first to get developer feedback quickly.
• Baseline and tier thresholds: Use separate thresholds for static bounds vs measurement results and for PR vs mainline checks.
• Automate evidence: Archive signed reports, tool versions, and binary hashes for certification readiness.
• Combine tools: Use both static (e.g., RocqStat-style path analysis) and measurement to get safety and realism.

Further reading and resources (2026-aware)

• Vector announcement on RocqStat acquisition (Jan 2026) — integration signals for unified timing+testing toolchains.
• Recent papers and industry reports on probabilistic WCET and CI-based continuous verification (2024–2026).
• Open-source measurement runners and QEMU harness examples on popular repos (search for wcet and timing-harness).

Final thoughts

The move to integrate WCET and timing analysis into CI/CD is no longer optional for teams building safety-critical embedded systems. With the 2026 trend toward vendor consolidation (Vector + RocqStat) and better CI tooling, you can automate timing verification without sacrificing developer velocity.

Start with reproducible builds, add fast static checks on PRs, and schedule authoritative HIL runs for mainline — then iterate on thresholds and automation. Treat timing as code: measurable, versioned, and continuously verified.

Call to action

Ready to try this in your CI? Clone the sample repo we prepared with a Docker-based WCET runner and GitHub Actions templates. Join the programa.club community to get the sample code, join a 2-week workshop on continuous timing verification, and share your CI pipelines for peer review.

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