Automating Software Verification Workflows: Integrating RocqStat with CI Pipelines

Cut verification cycle time without sacrificing safety: integrating timing analysis into CI/CD

Long verification cycles, fragmented toolchains, and manual WCET checks are slowing releases and threatening real-time guarantees. For engineering ops teams, the fix is not more manual review — it’s automation. This guide explains how to embed RocqStat timing analysis into modern CI/CD pipelines so your verification automation becomes repeatable, auditable, and fast.

The 2026 context: why timing analysis is now a pipeline-first concern

In 2026 the ecosystem for timing verification is consolidating. Notably, Vector Informatik acquired RocqStat’s StatInf technology in January 2026 to merge advanced timing analysis with established test toolchains. As Automotive World reported:

"Vector Informatik has acquired StatInf’s RocqStat software technology and expert team to strengthen its capabilities in timing analysis and worst-case execution time (WCET) estimation... Vector described a unified environment for timing analysis, WCET estimation, software testing and verification workflows." — Automotive World (Jan 16, 2026)

This consolidation reflects three trends engineering ops must accept:

• Safety-critical domains (automotive, aerospace, industrial) demand continuous timing assurance rather than episodic checks.
• Toolchains are moving toward unified, API-first platforms — making CI/CD integration feasible and expected.
• Organizations want measurement-driven verification: objective WCET numbers and trend analysis embedded in pipelines.

High-level architecture: where RocqStat fits in the CI/CD pipeline

Design your pipeline with a dedicated timing stage. The simplest pattern is:

1. Source build (deterministic compiler flags, reproducible artifacts)
2. Unit and integration tests
3. Static analysis / linting
4. Timing analysis (RocqStat)
5. Report generation & artifact upload
6. Gating / release promotion

RocqStat can run as a CLI tool, containerized service, or (when available) via VectorCAST integration. In CI workflows, treat RocqStat as a test runner that consumes compiled binaries and debug information and produces structured timing reports.

Key inputs and outputs

• Inputs: compiled binaries, map/symbol files, debug info (DWARF), target CPU model or measurement traces, configuration for loop bounds/annotations.
• Outputs: WCET estimates per function/task, timing path traces, JSON/XML reports, PDF summaries, and optional instrumentation traces.

Practical CI examples: GitLab CI and GitHub Actions

Below are practical job templates to run RocqStat during CI. Adapt to your runner (self-hosted for cross-compile, or cloud with appropriate toolchains).

Example: GitLab CI job (containerized)

stages:
  - build
  - test
  - timing

build:artifact:
  stage: build
  image: registry.example.com/toolchains/cross-compiler:2026
  script:
    - make clean && make all CFLAGS="-g -O2 -fno-inline"
  artifacts:
    paths:
      - build/*.elf
      - build/*.map

rocqstat:timing:
  stage: timing
  image: registry.example.com/rocqstat/runner:2026
  needs:
    - build:artifact
  script:
    - rocqstat analyze --binary build/target.elf --map build/target.map --cpu-model cortex-m7 --output reports/rocqstat.json
    - rocqstat report --input reports/rocqstat.json --format json --out reports/rocqstat-summary.json
  artifacts:
    paths:
      - reports/rocqstat-summary.json
  allow_failure: false
  rules:
    - if: '$CI_COMMIT_BRANCH == $CI_DEFAULT_BRANCH'  # enforce on main branch

Example: GitHub Actions job

name: CI
on: [push, pull_request]

jobs:
  build:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Build
        run: make all CFLAGS="-g -O2 -fno-inline"
      - name: Upload artifacts
        uses: actions/upload-artifact@v4
        with:
          name: elf-artifacts
          path: build/

  timing:
    runs-on: self-hosted
    needs: build
    steps:
      - name: Download artifacts
        uses: actions/download-artifact@v4
        with:
          name: elf-artifacts
      - name: Run RocqStat
        run: |
          docker run --rm -v ${{ github.workspace }}/build:/work rocqstat/runner:2026 \
            rocqstat analyze --binary /work/target.elf --map /work/target.map --cpu-model riscv-rv32 --output /work/reports/rocq.json
      - name: Upload timing result
        uses: actions/upload-artifact@v4
        with:
          name: rocqstat-report
          path: build/reports/rocq.json

Runtime modes: CLI, container, and API

Use the mode that matches your CI constraints.

• CLI — best for self-hosted runners with toolchains installed. Simple, low-latency.
• Container — ideal for reproducibility and cloud runners. Prebuilt images encapsulate RocqStat and its runtime models.
• API — when RocqStat is offered as a service (or when integrated into VectorCAST), use HTTP calls to submit artifacts and poll results; useful for centralized verification services.

Calling RocqStat via API (example curl)

curl -X POST https://rocqstat.example.com/api/v1/analyze \
  -H "Authorization: Bearer $ROCQ_API_TOKEN" \
  -F "binary=@build/target.elf" \
  -F "map=@build/target.map" \
  -F "cpu=cortex-m33" \
  -F "callback=https://ci.example.com/webhooks/rocqstat"

# Polling
curl -H "Authorization: Bearer $ROCQ_API_TOKEN" https://rocqstat.example.com/api/v1/jobs/$JOB_ID

Gate rules and verification automation strategies

Define explicit, automated gating rules for timing regressions. Example gating rules:

• Fail the pipeline if any task WCET increases by > 5% versus baseline.
• Block merges when a critical real-time path’s WCET exceeds its deadline.
• Create non-blocking warnings for minor regressions and open Jira tickets automatically for > 10% regressions.

Implement these using the RocqStat JSON report and a small validation script that compares against stored baselines in object storage (S3) or your CI artifact store.

Example gating script (pseudo)

# Load baseline and new report (JSON)
baseline = load_json('s3://verif-baselines/target-main/rocqstat-baseline.json')
current = load_json('reports/rocqstat-summary.json')

for func in current['functions']:
  delta = (current[func]['wcet'] - baseline[func]['wcet']) / baseline[func]['wcet']
  if delta > 0.05 and func in critical_paths:
    exit(1)  # fail CI

Notifications and automation recipes (Zapier/Make/API)

Use automation platforms or native webhooks to fast-track issue creation and stakeholder alerts.

• Zapier recipe: New RocqStat failure → create Jira ticket → post Slack channel with link to report.
• Make (Integromat) scenario: On artifact upload → parse JSON → update Confluence timing trends page and create a remediation card in Trello.
• API-driven: CI webhook receives RocqStat callback → validation service runs gating logic → emits events to event bus (Kafka) → downstream dashboards update.

Example Zapier flow outline:

1. Trigger: HTTP webhook (RocqStat or CI sends job result)
2. Action: Formatter parses JSON and checks thresholds
3. Action: Create Jira issue if threshold breached
4. Action: Post Slack notification to #timing-alerts with artifacts URL

Best practices for accurate WCET in CI

• Reproducible builds: use pinned compiler versions and deterministic flags so RocqStat sees identical binaries across runs.
• Symbol fidelity: ensure DWARF and map files are generated; without symbols, path mapping and per-function WCET degrade.
• Target CPU models: use accurate microarchitectural models or measurement traces; mis-modeled caches or pipelines produce optimistic WCETs.
• Incremental analysis: analyze only changed modules to reduce runtime; keep global verification nightly for full-system WCETs.
• Baselines and trend analysis: store per-commit WCETs and plot trends to detect creeping regressions early.

Handling uncertainty: combining static estimation and measurements

WCET workflows often blend static analysis (conservative estimates) with measurement-based evidence. Practical strategy:

1. Run static WCET (RocqStat) in CI to catch regressions and worst-case paths.
2. Periodically run hardware-in-the-loop (HIL) or instrumented tests to collect execution time traces for key paths.
3. Use measurement data to tune microarchitectural models (e.g., cache behavior) in RocqStat or to justify margins for safety certification.

Document the chosen combination in your verification plan so safety auditors see the evidence chain.

Observability, metrics, and measuring ROI

Metrics matter to engineering ops. Track these KPIs after RocqStat integration:

• Verification cycle time: time from commit to timing report availability.
• WCET regression rate: percentage of builds that trigger timing regressions.
• Mean Time To Verification (MTTV): time to detect and resolve timing regressions.
• False positive rate: number of gating failures overridden after investigation.
• Coverage of timing-critical functions: percent of annotated critical paths analyzed automatically.

Use Grafana or your CI’s analytics to show trends for these metrics and tie improvements to reduced release delays and lower remediation cost — your ROI justification when expanding timing automation across teams.

Security, compliance and traceability

For regulated industries, pipelines must be auditable:

• Sign and store artifacts (binaries, map files, RocqStat reports) with immutable object storage.
• Use role-based access to the RocqStat service and rotate API keys through your secrets manager.
• Publish verification evidence bundles for audits (commit hash, compiler flags, RocqStat JSON, HIL traces).

These steps help meet expectations from standards like ISO 26262 (automotive) or DO-178C (aerospace) when timing analysis forms part of safety arguments.

Common integration issues and troubleshooting

Missing debug symbols or stripped binaries

Symptoms: RocqStat reports coarse timings or cannot attribute WCET to functions. Fix: build with DWARF and separate symbol packages if you must strip production artifacts.

Model mismatch (CPU/cache behavior)

Symptoms: optimistic WCET on hardware runs. Fix: align RocqStat’s CPU model with actual silicon or supply hardware traces for model calibration.

Long analysis times

Symptoms: pipeline latency spikes. Fix: use incremental analysis, run per-module or only changed modules on PRs, reserve full-system runs for nightly jobs.

False positives from conservative annotations

Symptoms: frequent gating fails that consume engineering time. Fix: tune loop bounds and use measured evidence to loosen overly conservative annotations where safe.

Case study: reducing verification latency in an automotive ECU team

Context: mid-size ECU team (20 engineers) moved RocqStat into their GitLab CI in 2025 and expanded after Vector’s 2026 acquisition because of improved integration options. Their outcomes within 6 months:

• Verification cycle time dropped from 8 hours to 2.5 hours for PR-level checks by running incremental RocqStat analysis on changed modules.
• Nightly full-system WCET runs remained but were automated and stored; the team reduced manual WCET regression investigations by 60%.
• Auditors accepted the new evidence bundle format (commit + RocqStat JSON + HIL trace) as part of the safety case, removing a major release gate.

Takeaway: start small, measure, then scale. The team began with per-PR analysis on critical modules and expanded to full-system nightly verification after baselines stabilized.

Future-proofing your pipeline (2026–2028 predictions)

• Expect deeper integrations as Vector consolidates RocqStat into VectorCAST and other toolchains — plan for API-based orchestration.
• Cloud-native timing services and federated verification (on-prem HIL + cloud analysis) will become common; design pipelines to hybridize.
• AI-assisted path selection and model tuning will reduce WCET pessimism; prepare for model governance and explainability controls.

Rollout checklist for engineering ops (10-step)

1. Identify critical paths and compile them with debug info.
2. Choose RocqStat mode (CLI, container, or API) based on runners and security policy.
3. Create a per-PR incremental timing job and a nightly full-system job.
4. Store RocqStat JSON reports as CI artifacts and mirror to S3 for baselines.
5. Implement gating scripts to compare against baselines and enforce thresholds.
6. Hook failures to automation (Zapier/Make or native webhooks) to create tickets and alerts.
7. Instrument HIL runs to collect traces for model calibration.
8. Define audit bundles and retention policy for compliance.
9. Track KPIs (verification time, regression rate); present weekly to stakeholders.
10. Iterate: tune models and thresholds based on measurement feedback.

Final recommendations

Embedding RocqStat into your CI/CD pipeline transforms timing verification from an occasional manual task into an automated, auditable part of development. Start with a guarded per-PR integration, align your build process for reproducibility, and automate gating and notifications. Use baselines and trend dashboards to catch regressions early and justify verification investment with measurable ROI.

Ready to shorten verification cycles? If you manage engineering ops in a safety-focused environment, begin a 4-week proof-of-concept: containerize RocqStat, add a per-PR timing job, and put a basic gate in place. For hands-on help building the pipeline, baselines, and automation recipes (Zapier/Make/API integrations), contact our team — we specialize in operationalizing timing analysis and WCET verification for production toolchains.

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