embeddedintegrationtooling

Integrating VectorCAST + RocqStat: Best Practices for Real-time Embedded Teams

UUnknown

2026-02-15

9 min read

Practical guide to integrating VectorCAST and RocqStat into embedded CI for reliable WCET, automated gating, and dashboarded metrics.

Integrating VectorCAST + RocqStat: Best Practices for Real-time Embedded Teams

Hook: If your embedded team is struggling with fragmented verification workflows, flaky timing estimates, and CI pipelines that don’t surface regressions in WCET or timing budgets, the VectorCAST + RocqStat combination (following Vector’s Jan 2026 acquisition of RocqStat) can change that. This guide shows how to integrate timing analysis and software verification into a single, automated toolchain that fits modern embedded CI, tool runners, and reporting dashboards.

Why integrate now (2026 context)

Late 2025 and early 2026 brought clear market signals: software-defined systems—especially in automotive—demand both precise timing guarantees and automated verification across CI. Vector’s acquisition of RocqStat signals a consolidation of WCET estimation and test automation into VectorCAST, enabling teams to move from manual handoffs to integrated, auditable pipelines.

"Timing safety is becoming a critical ..." — Eric Barton, Senior VP, Code Testing Tools, Vector (Jan 2026 announcement)

What this guide delivers

Actionable, step-by-step patterns for integrating VectorCAST and RocqStat into embedded CI systems, including:

Architectural integration patterns (in-pipeline, sidecar, and out-of-band)
Concrete CI examples (GitLab CI and GitHub Actions)
Data flow: artifacts, metrics, dashboards
Security, reproducibility and compliance considerations
Operational best practices and troubleshooting

High-level integration patterns

Choose one of three patterns depending on team size, RTOS/hardware complexity, and release cadence.

1) In-pipeline (recommended for rapid feedback)

Run VectorCAST tests and RocqStat timing analysis as part of each merge pipeline. Provide pass/fail gating based on both functional tests and timing budgets.

Pros: fast feedback, immediate regression detection
Cons: heavier CI resources, may require hardware-in-the-loop (HIL) or emulation

2) Sidecar analysis (recommended for heavy timing runs)

Offload expensive RocqStat runs to a sidecar job or dedicated timing cluster. The CI job schedules the analysis and polls for results; results are merged back as artifacts.

Pros: isolates expensive runs, better scaling for large suites
Cons: slightly more complex orchestration

3) Out-of-band nightly/PR gating

Keep quick functional VectorCAST runs in PRs, and schedule full WCET and deep timing analysis nightly. Use nightly runs to detect slow drift or architectural regressions.

Pros: low cost during day-to-day development
Cons: slower detection of timing regressions

Preparation: align artifacts and interfaces

Before you write pipeline scripts, agree on the contract between VectorCAST and RocqStat and how results are emitted and consumed.

Key artifacts

Build artifacts: ELF, map files, object files, compiler/linker flags
Test artifacts: VectorCAST reports (JUnit/XML, HTML), coverage reports
Timing artifacts: trace capture, binary images for static analysis, RocqStat output (JSON/XML)
Metadata: SBOM, commit hash, toolchain version, and hardware configuration

Define a timing-result schema

Standardize a lightweight JSON schema that RocqStat will export and your dashboards will ingest. Example fields:

{
  "commit":"",
  "target":"board-name",
  "binary":"app.elf",
  "analysis":{
    "function_wcet":{
      "main_task": 1234.5,
      "comm_handler": 12.2
    },
    "system_wcet_ms": 1450.0,
    "confidence": 0.95
  }
}

Keep the schema small, immutable, and versioned.

CI integration: concrete examples

Below are two practical CI job patterns: one for GitLab CI and one for GitHub Actions. They assume VectorCAST and RocqStat are available as CLI tools or Docker images (recommended for reproducibility).

GitLab CI job (in-pipeline)

stages:
  - build
  - test
  - timing

build:
  stage: build
  image: gcc:12
  script:
    - make clean all
  artifacts:
    paths:
      - build/app.elf
      - build/*.map

vectorcast-test:
  stage: test
  image: vectorcast/vectorcast-cli:2026.1
  script:
    - vectorcast run --project tests/vcast_project.vc --output reports/vcast_report.xml
  artifacts:
    paths:
      - reports/vcast_report.xml

rocqstat-analysis:
  stage: timing
  image: vectorcast/rocqstat-cli:2026.1
  dependencies:
    - build
  script:
    - rocqstat analyze --binary build/app.elf --map build/app.map --out reports/rocqstat.json
  when: on_success
  artifacts:
    paths:
      - reports/rocqstat.json

Notes: use Docker images published internally so the toolchain versions are fixed and reproducible.

GitHub Actions job (sidecar pattern)

name: CI
on: [push, pull_request]

jobs:
  build:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Build
        run: make all
      - name: Upload build
        uses: actions/upload-artifact@v4
        with:
          name: app-binaries
          path: build/app.elf

  rocqstat-run:
    runs-on: ubuntu-22.04
    needs: build
    steps:
      - name: Download build
        uses: actions/download-artifact@v4
        with:
          name: app-binaries
      - name: Run RocqStat
        uses: docker://vectorcast/rocqstat-cli:2026.1
        with:
          args: analyze --binary build/app.elf --out rocqstat.json
      - name: Upload results
        uses: actions/upload-artifact@v4
        with:
          name: rocqstat-results
          path: rocqstat.json

Notes: for sidecar orchestration and messaging between CI and timing clusters, consider patterns from edge message brokers and event-driven scheduling.

Connecting results to dashboards and gates

CI jobs should do two things with RocqStat and VectorCAST outputs: store artifacts (for audit and compliance) and export metrics for dashboards and gates.

Exporting metrics

Produce a metrics payload from the RocqStat JSON, e.g., system_wcet_ms and highest_function_wcet_ms.
Push these metrics to a metrics endpoint: Prometheus pushgateway, InfluxDB, or VectorCAST reporting API.
Use Grafana (or VectorCAST dashboards) to visualize trends, per-PR deltas, and confidence intervals.

Gating policy examples

Fail PR if system_wcet_ms increases > 5% from main branch median.
Block merge if any function WCET exceeds its budget (defined in metadata).
Allow merciful merges for minor timing regressions but open a tracking ticket automatically.

Instrumentation and trace capture

Accurate WCET estimation often needs trace inputs or compiler-assisted annotations. Decide if you will use:

Static analysis: full binary + map file for RocqStat’s flow analyses (and consider caching strategies to avoid redoing expensive work)
Trace-based profiling: hardware ETM traces or software tracepoints for executing hot paths
Compiler instrumentation: edge counters or path logs to feed hybrid analysis

Best practice: collect a minimal set of runtime traces in CI (on a representative board or simulator) and attach them to the RocqStat run to improve confidence without needing full-system logging every time. Store trace capture and reference it in your edge+cloud telemetry strategy so you can aggregate and analyze traces from devices and simulators.

Scaling and performance

Timing analyses can be compute intensive. Use these strategies to keep CI fast:

Parallelize: split analyses by module or function when supported.
Cache: reuse intermediate static analysis artifacts when commits don’t change relevant code paths.
Tiered runs: quick checks in PRs and full analysis on merge/nightly.
Dedicated hardware: run heavy analyses on high-memory instances or on-prem nodes with privileged access to hardware traces.

Security, compliance, and reproducibility

For safety-critical systems you need an auditable chain from source to timing claim.

Persist all artifacts (binaries, map files, RocqStat JSON, VectorCAST reports) with immutable storage and retention policy.
Record tool versions (VectorCAST and RocqStat versions), compiler and linker flags in metadata.
Generate an SBOM for the build and sign critical artifacts (e.g., app.elf) to guarantee provenance. Consider compliance guidance such as FedRAMP and procurement guidance when you operate in regulated environments.
Enable role-based access and encryption-at-rest for stored trace and timing outputs.

Operational playbook: daily and incident

Daily checks

Monitor timing trend dashboards for sudden increases or growing variance.
Review PRs with any timing metric change flagged by the gate.
Run quick smoke analysis on hardware for release candidates.

Incident triage

Reproduce the issue by running VectorCAST+RocqStat locally with the failing commit and the same tool versions.
Collect runtime traces and compare to the baseline traces.
Use the RocqStat per-function report to identify candidate regressions and revert if needed.

Troubleshooting common problems

1) Noisy variance in WCET estimates

Cause: non-deterministic inputs or insufficient trace coverage. Mitigation: fix seeds for simulated inputs, capture representative traces, and increase confidence settings in RocqStat.

2) CI job exceeds time limits

Cause: running full static timing on every PR. Mitigation: switch to quick checks, use the sidecar pattern, or increase CI time limits for jobs that produce signed artifacts.

3) Maps mismatch between build and analysis

Cause: mismatched compiler flags, link-time optimizations, or stripped symbols. Mitigation: ensure the exact binary and map file used for analysis are produced and stored as a single artifact.

Real-world example (mini case study)

Background: An automotive ECU team introduced VectorCAST for unit and integration testing in 2024. After RocqStat integration in 2026 they:

Added a RocqStat stage to their nightly pipeline (sidecar) and a lightweight timing check in PRs.
Exported system_wcet_ms and top-3 function WCET metrics to Prometheus and visualized regressions in Grafana.
Stopped shipping a release once timing regression exceeded 3% without an approved plan of mitigation.

Outcome: They reduced production timing incidents by 65% in six months and reduced mean time to detection of timing regressions from weeks to hours.

Advanced strategies and 2026 trends

Expectations for 2026 and beyond:

Tighter integration: VectorCAST will progressively absorb RocqStat flows—expect richer APIs and single-pane reporting (VectorCAST as the canonical dashboard for both functional and timing verification).
Hybrid analysis: Combining static WCET with trace-informed profiles is becoming standard, increasing confidence while lowering false positives.
DevSecOps for timing: Timing analysis is moving into security/compliance conversations—trace data must be protected and provenance recorded.

Adopt these strategies now to be ahead of the curve: automate as much as possible, make timing metrics visible, and treat timing like testable code.

Checklist to get started (30/60/90 day plan)

First 30 days

Install VectorCAST and RocqStat CLI/Docker in CI.
Define timing-schema and artifact contract.
Run a manual RocqStat analysis on a release binary and store artifacts.

30–60 days

Integrate quick timing checks into PR pipelines.
Publish metrics to a dashboard and set simple alert thresholds.
Create a gating policy and communicate it to the team; bake gating into your developer experience so merges and rollbacks are predictable.

60–90 days

Automate sidecar/full RocqStat runs for merge/nightly pipelines.
Instrument representative traces and add them to analysis runs to increase confidence.
Audit the artifact retention and signing to satisfy compliance.

Closing guidance

Integrating VectorCAST with RocqStat transforms timing from an ad-hoc, late-stage activity into a first-class part of CI. The acquisition in Jan 2026 (reported across industry press such as Automotive World) means the integration roadmap is accelerating—teams that architect their pipelines now will gain early advantages in reliability, auditability, and speed to market.

Actionable takeaways:

Start small: add a RocqStat smoke run to your existing VectorCAST CI jobs.
Standardize the timing result schema and push metrics to your dashboard.
Enforce reproducibility: store binaries, maps, and tool versions for every run.

Get started — call to action

Ready to consolidate timing analysis and verification into a single automated workflow? Begin with our 30/60/90 checklist, or contact your Vector representative to trial the integrated VectorCAST + RocqStat pipelines. If you want a deeper, hands-on blueprint tailored to your CI (GitLab, Jenkins, or GitHub Actions), reach out for a custom integration playbook and scripts that fit your hardware and compliance needs.

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