Bringing Real-Time Verification into CI for Edge Devices

Shipping reliable edge firmware is failing at the last mile: timing bugs slip through CI

If your CI builds green but devices in the field miss deadlines, you already know the pain: timing failures and missed real-time guarantees surface late, after costly releases or recalls. For teams building edge devices in South Asia and beyond, the problem is magnified by remote test rigs, compliance constraints, and scarce local tooling in Bengali. This guide shows a practical, production-ready pipeline to bring real-time verification into CI for embedded projects using timing analysis tools like RocqStat and VectorCAST, artifact storage, automated test orchestration, and cloud-native logging and metrics.

Why add timing analysis to CI now (2026 trends)

By early 2026 the industry is converging on unified verification stacks. Vector Informatik's acquisition of StatInf’s RocqStat (announced Jan 16, 2026) signals a clear market shift: timing analysis and WCET estimation are moving into mainstream code-testing toolchains (see Automotive World coverage). Expect tool vendors to integrate static and dynamic timing verification into standard CI systems flows. At the same time, observability for edge telemetry has matured—OpenTelemetry and cloud metrics are now common for embedded telemetry, and CI systems support richer artifact and metrics workflows.

That means teams can and should shift timing verification left: from late-stage system testing to automated CI gates. The result is faster feedback, fewer late reworks, and measurable trends for timing regressions.

Core concepts and goals

• Timing analysis: static and dynamic techniques to estimate Worst-Case Execution Time (WCET) and identify deadline misses.
• Embedded CI: CI pipelines that run cross-compiles, unit/integration tests, timing instrumentation, and hardware-in-the-loop (HIL) or emulation steps.
• Artifacts: deterministic storage of binaries, trace files, coverage and timing reports (S3 / MinIO / artifact registry).
• Automation: orchestration that runs tests on emulator/HIL, invokes RocqStat/VectorCAST headless, and records results.
• Cloud logging & metrics: time-series metrics for WCET and traces for root-cause, with dashboards and alerts (OpenTelemetry-first observability).

Overview: a practical CI pipeline

This end-to-end pipeline is proven in field projects and fits GitLab CI / GitHub Actions / Jenkins with minor adjustments. Key stages:

1. Source build & cross-compile
2. Unit tests & VectorCAST host-run
3. Instrumented timing run (RocqStat / VectorCAST integration)
4. Collect artifacts (binaries, trace, timing reports)
5. Push artifacts to S3-compatible storage and register in artifact index
6. Publish metrics & logs to cloud observability (OpenTelemetry -> Prometheus/Grafana Cloud)
7. Gate merge based on timing SLAs, WCET regressions, and test pass/fail

Pre-requisites and practical constraints

• Access to VectorCAST and RocqStat: both are commercial—set up an automated license server or use CI nodes with floating licenses. Coordinate with vendor for headless CLI usage and license entitlements.
• Cross-compilation toolchain encapsulated in a container or build image for reproducibility (GCC/clang, ARM toolchains, vendor SDKs).
• Emulation/HIL: QEMU or Renode for early timing proxies; production verification on HIL with the target CPU and trace capture (ETM, SWO, or hardware tracer).
• Artifact storage: S3-compatible endpoint (AWS S3, DigitalOcean Spaces, or self-hosted MinIO) with lifecycle rules and access control for compliance/data residency.
• Metrics & logging backends: use OpenTelemetry for metrics and trace export, Prometheus/Grafana or a SaaS (Grafana Cloud, Datadog) for dashboards and alerts.

Step-by-step pipeline implementation

1. Build stage: deterministic and cache-friendly

Use a reproducible build image. Keep toolchain versions in the repository. Example responsibilities for build job:

• Pull build image with toolchain and VectorCAST CLI installed
• Run cross-compile unit artifacts and build test harness
• Store binaries and build metadata (git SHA, build time, toolchain version)

Store build metadata as JSON artifact with a stable name: build-metadata-<sha>.json.

2. Unit and integration tests with VectorCAST

VectorCAST supports headless runs via CLI. In CI, call VectorCAST to execute unit tests and produce coverage and trace files. Important knobs:

• Run in headless batch mode (VectorCAST batch/console options)
• Set deterministic seeds for tests that use randomness
• Export test results as JUnit XML for CI reporting

Save coverage and VectorCAST project artifacts to the artifact storage for later analysis.

3. Timing analysis: integrating RocqStat

RocqStat provides static & hybrid WCET analysis. With Vector's 2026 acquisition of RocqStat technology, expect tighter CLI integration in VectorCAST; until vendor releases merged workflows, you can orchestrate a RocqStat run in CI as a separate step that consumes the same binaries and trace data from VectorCAST.

Practical sequence:

1. Generate CFGs and binary traces (VectorCAST can export instrumentation needed for RocqStat)
2. Run RocqStat CLI with map/symbol files and target architecture model
3. Produce WCET report (JSON or XML)
4. Fail the CI job if computed WCET > configured threshold or if analysis fails

Tip: keep a wcet-baseline.json per release branch and compare new analysis results to detect regressions.

4. Hardware-in-the-loop and emulation fallback

Where possible, run timing verification on representative hardware. Use a small HIL farm with reserved runners. If HIL is scarce, use emulation (Renode/QEMU) for earlier feedback—document the expected delta between emulated and real WCET and treat emulation as a fast pre-check.

Automate HIL access via test rig orchestration layers (SSH, lab automation frameworks like Jenkins + Labgrid). Ensure locking of physical rigs in CI so concurrent jobs don’t collide.

5. Artifact storage and index

All outputs must be stored reliably with metadata for auditing:

• binaries/sha/ -> firmware-<sha>.bin
• vectorcast-reports/ -> junit.xml, coverage.zip
• rocqstat-wcet/ -> wcet-<sha>.json
• traces/ -> etf/etl/trace files
• metadata/ -> build-metadata.json

Use S3 object tags or a small metadata DB (Postgres or DynamoDB) to create an artifact index for fast lookup by commit SHA and test run id. This helps when debugging regressions.

6. Metrics & logging: capture timing as first-class telemetry

Export timing results and test outcomes as metrics. Key metrics to collect:

• wcet_ms: WCET per function/component
• p95/p99_latency_ms: percentile measurements from dynamic runs
• timing_regression: boolean indicating regression vs baseline
• test_failures_total: aggregated unit/integration failures

Instrumentation pattern:

1. RocqStat/VectorCAST produce a JSON report
2. CI step runs a small exporter script to convert JSON -> OpenTelemetry metrics
3. Push metrics to a metrics agent (Prometheus pushgateway or OTLP endpoint)
4. Generate alerts when wcet_ms crosses SLO thresholds or trends upwards over N builds

python tools/metrics_exporter.py --wcet reports/wcet-12345.json --otlp-endpoint https://otlp.example.com:4317

7. Gate pulls and merge checks

Make timing analysis part of PR gates. Use rules like:

• Fail PR if any unit tests fail
• Fail PR if WCET increases > 5% for the modified component
• Block merge if dynamic latency p99 > SLO

When a change affects timing, require an owner sign-off with a justification or mitigation (different scheduling, reduced load, more CPU budget).

Sample GitLab CI steps

This is a minimal example showing core steps (build, vectorcast, rocqstat, upload, metrics). Adapt tokens and endpoints for your environment.

stages:
  - build
  - test
  - timing
  - archive

build:
  stage: build
  image: registry.example.com/embedded-build:2026.01
  script:
    - make all
    - jq -n --arg sha "$CI_COMMIT_SHA" --arg v "1.2.3" '{sha:$sha,toolchain:$v}' > build-metadata.json
  artifacts:
    paths:
      - build/*.bin
      - build-metadata.json

vec_test:
  stage: test
  image: registry.example.com/vector-tools:latest
  script:
    - vectorcast-cli run --project project.vcp --output junit.xml
  artifacts:
    when: always
    paths:
      - junit.xml
      - coverage.zip

rocqstat:
  stage: timing
  image: registry.example.com/rocqstat-cli:latest
  script:
    - rocqstat analyse --binary build/firmware.bin --map build/firmware.map --output wcet.json
    - python tools/metrics_exporter.py --wcet wcet.json --otlp-endpoint "$OTLP_ENDPOINT"
    - test $(jq '.wcet_ms' wcet.json) -le 50 || (echo "WCET limit exceeded" && exit 1)
  artifacts:
    paths:
      - wcet.json

archive:
  stage: archive
  image: amazon/aws-cli
  script:
    - aws s3 cp wcet.json s3://artifacts/myproject/wcet-/$CI_COMMIT_SHA.json
    - aws s3 cp build/firmware.bin s3://artifacts/myproject/binaries/firmware-$CI_COMMIT_SHA.bin

Dealing with licensing and headless operation

Commercial tools need license planning in CI:

• Use a license server reachable by CI runners or preallocate license seats on dedicated runners.
• For isolated HIL runners with no external access, request an offline license/token for CI usage.
• Automate license checkout/checkin in job scripts to avoid leakage.

Work with vendor support to run VectorCAST and RocqStat headless and to generate machine-readable reports for CI consumption.

Advanced strategies and future-proofing (2026+)

Adopt these strategies as tooling evolves:

• Baseline and trend detection: preserve wcet baselines per release, and use rolling-window trend detection to flag slow erosions early.
• AI-assisted test case generation: use AI to generate targeted tests that exercise worst-case paths—vendors are shipping AI-guided path exploration in late 2025 and early 2026.
• Open telemetry for embedded: embed OpenTelemetry SDKs (or use collector sidecars) to unify telemetry across CI and devices. This simplifies correlating CI analysis with field telemetry.
• Hybrid verification: combine static RocqStat WCET with measured dynamic traces to narrow bounds—this hybrid approach is gaining traction post-2025.
• Synthetic load testing: simulate high-interrupt or memory-congested scenarios in CI to validate timing under stress.
• Artifact immutability and signing: sign artifacts and records for compliance and audits (important for automotive/pharma/regulated domains).

Example: real-world mini case study (Bengal edge device team)

Background: A Kolkata-based IoT vendor saw sporadic deadline misses on a camera-based inference edge node in field tests. They adopted this pipeline in Q3–Q4 2025 and ran a pilot in Jan 2026 when Vector integrated RocqStat into its stack.

Outcomes after 10 sprints:

• Average time-to-detect timing regressions dropped from 18 days to under 2 hours.
• Field deadline misses reduced by 82% in the following release cycle.
• CI-run WCET metrics exposed a scheduler contention issue; fixing it reduced p99 latency by 35%.
• All artifacts and reports were stored in an S3 endpoint hosted in Kolkata region to satisfy data residency policies.

This demonstrates how adding automated timing analysis to CI is not just a validation checkbox but a productivity multiplier.

Operational checklist before full rollout

• Confirm VectorCAST/RocqStat CLI automation and license support with vendor.
• Standardize artifact naming and metadata (SHA, build env, runner id).
• Provision HIL or emulation runners and ensure exclusive locking in CI.
• Set realistic SLOs for WCET and p95/p99 based on system design margins.
• Create dashboards and alerts for timing regressions and trends.
• Document testing policies in your repo and provide Bengali-language runbooks for local teams and operators.

Troubleshooting common issues

False positives from emulation

Emulators often under-approximate hardware timing. Always label emulation results and require HIL verification for gating production merges.

License contention in CI

Lock licenses to dedicated, long-running runners for timing jobs or schedule timing jobs off-peak. Use license-checkout scripts to minimize wait times.

Large trace files

Compress traces and store them separately with lifecycle rules. Retain full traces for failed runs and smaller summaries for green runs.

Actionable takeaways

• Shift timing verification left: integrate RocqStat/VectorCAST into CI to detect WCET regressions early.
• Persist artifacts (binaries, traces, reports) to S3 and keep metadata for reproducible audits.
• Export timing results as OpenTelemetry metrics—build dashboards and alert on regressions, not just failures.
• Use emulation for speed and HIL for final verification; automate rig access and locking in CI.
• Plan licensing for commercial tools—automate checkout and consider dedicated runners for heavy tools.

“With Vector’s 2026 integration of RocqStat capabilities, expect embedded CI pipelines to standardize on combined testing-and-timing flows—your CI should be ready.” — industry coverage (Automotive World, Jan 16, 2026)

Final recommendations and next steps

Start small: add a timing analysis job that runs on each merge request but only gates the branch when confidence is high. Maintain a baseline repository of WCETs and tune thresholds over several releases. Combine static RocqStat results with real-device traces from VectorCAST to get both conservative bounds and practical measurements.

If you operate in Bengal, ensure your artifact storage and metrics tenants meet local data residency and language requirements—provide Bengali runbooks and on-call support to shorten incident resolution times.

Call to action

Ready to bring real-time verification into your CI pipeline? Start with a 2-week pilot: add a headless VectorCAST unit test job and a RocqStat analysis step to one branch, push artifacts to an S3 bucket in your region, and hook metrics to Grafana. If you want a prebuilt GitLab CI template and a Bengali runbook tailored to Bengal-region compliance, contact our Bengal.cloud engineering team for a hands-on workshop and a pipeline review.

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