Case Study: From Raw Web Crawl to WCET Verification—Integrating Timing Analysis into CI for Embedded Software

Hook: When timing failures are as costly as functional bugs

For development teams building real-time embedded control or high-throughput data pipelines, a passing unit test isn't enough. Missed deadlines, tail-latency spikes, or unseen worst-case paths can break SLAs and safety guarantees. In 2026 the industry is converging on a practical answer: integrate timing analysis into CI/CD the same way you integrate unit and integration tests.

Executive summary — What you'll get

This case study shows how to bring RocqStat/VectorCAST-style timing analysis concepts into a modern CI pipeline to verify time-sensitive code. You will see a real-world workflow adapted for embedded software and for data pipelines that must meet SLAs, including:

• Why timing verification matters in 2026 (regulatory and tech trends)
• Hybrid analysis strategy: static WCET + measurement-based validation
• Concrete CI implementation pattern with examples (build, bench, analyze, gate)
• Benchmarking and reporting best practices for SLAs (p95/p99/WCET)
• Operational checklist and next steps

Why now — 2026 context and trends

Late 2025 and early 2026 saw acceleration in tooling and business activity around timing verification. In January 2026 Vector announced it acquired RocqStat's team and technology to integrate timing analysis into the VectorCAST suite — a clear market signal: timing safety is now a mainstream verification concern for automotive and other safety-critical domains. At the same time, large-scale data platforms (driven by OLAP/analytics growth and vendors like ClickHouse) pushed teams to treat latency guarantees as first-class requirements, not post-hoc optimizations.

Why that matters for you: embedded systems and data pipelines face the same core problem — an unpredictable worst-case path causes system-level SLA violations. You need repeatable tools and CI checks to catch regressions early and prevent costly remediation.

Core concepts: WCET, timing analysis, and SLA mapping

WCET (Worst-Case Execution Time) is a conservative bound on how long a piece of software can take to run on a target platform. Classical WCET tools (and RocqStat-style methods) use a mix of static analysis, control-flow/path analysis, and measured execution times to compute safe bounds.

For data pipelines, map WCET concepts to stage-level worst-case latency and end-to-end SLA budgets. Instead of CPU cycles, you may measure batch processing time, ingestion-to-query latency, or microservice tail-latency.

Key properties to verify in CI

• Deterministic bounds: WCET or conservative latency bound for critical routines.
• Statistical guarantees: p95/p99 measurements with confidence intervals for non-deterministic components.
• Regression protection: Gates that fail builds when timing metrics exceed thresholds.
• Traceability: Link timing violations to commits, tests, and environment.

Case study overview: Embedded control loop + analytics pipeline

We present a combined example: an embedded control application (motor controller) and an accompanying analytics pipeline that consumes telemetry and must respond under SLA. The project uses a hybrid timing verification strategy inspired by RocqStat/VectorCAST:

1. Static analysis to identify worst-case paths and produce conservative bound candidates.
2. Instrumentation-driven measurement to validate and tighten bounds on target hardware.
3. Statistical tail modelling (p95/p99/EVT) for non-deterministic I/O or cloud services in pipelines.
4. CI enforcement: automatic gating of merge requests if timing budgets are exceeded.

Platform and tools assumed

• Cross-compiled embedded firmware (ARM Cortex-M / Linux RT examples)
• CI system: GitHub Actions / GitLab CI / Jenkins
• Timing tools: static WCET analyzer (RocqStat-style) + measurement harness (perf, tracer, hardware timers)
• Data pipeline: containerized stream processors and ClickHouse-like OLAP backend for benchmarks

Step-by-step integration into CI

Below is a practical pipeline you can adopt. Keep each step short and automatable.

1) Build reproducibly on CI

Ensure deterministic builds and cross-compilation artifacts. Use pinned toolchains and containerized build images so CI timing runs are stable.

• Lock compiler, linker, and SDK versions.
• Produce symbolized binaries and map files for static analysis.

2) Run static timing analysis (RocqStat-style)

Run a static analysis pass in CI to compute candidate WCETs or identify longest control-flow paths. This step is fast compared to exhaustive measurement and gives immediate conservative bounds you can use as a first gate.

3) Deploy controlled measurement runs

Deploy the artifact to a hardware farm or simulator that mirrors production timing characteristics. Run an automated harness to exercise inputs that drive critical paths. Collect high-resolution timestamps, branch traces, and hardware counters.

4) Apply hybrid reconciliation

Compare static WCET candidate with measured maxima. Use statistical methods to reconcile gaps:

• If measured max >= static WCET -> static model needs updating; flag as fail.
• If measured max < static WCET -> tighten the bound if measurement coverage is sufficient.
• When I/O or non-determinism dominates, compute p99/p999 estimates using EVT or block maxima methods and combine with static bounds for CPU-bound segments.

5) Gate merges and releases

Fail CI when WCET or p99 exceeds configured budgets. Provide actionable diagnostics: offending function, input trace, assembly path, and links to profiling artifacts.

Example CI job (GitHub Actions YAML)

# Timing analysis job snippet (simplified)
name: timing-verification
on: [push, pull_request]

jobs:
  timing:
    runs-on: ubuntu-22.04
    steps:
      - name: Checkout
        uses: actions/checkout@v4

      - name: Build firmware
        run: |
          docker run --rm -v ${{ github.workspace }}:/src embedded/toolchain:2026 \
            /bin/bash -lc "cd /src && make clean && make all"

      - name: Static timing analysis
        run: |
          ./tools/rocqstat_analyzer --input build/firmware.elf --out artifacts/wcet.json

      - name: Deploy to test hardware
        run: ./ci/deploy_to_lab.sh artifacts/firmware.bin lab-node-01

      - name: Run measurement harness
        run: |
          ./ci/timing_harness.sh --iterations 1000 --out artifacts/measurements.csv

      - name: Analyze measurements
        run: |
          python ci/analyze_timing.py artifacts/wcet.json artifacts/measurements.csv --threshold-ms 10

      - name: Upload artifacts
        uses: actions/upload-artifact@v4
        with:
          name: timing-artifacts
          path: artifacts/

Key idea: leave static analysis to the analyzer, run measurements on representative hardware, and then reconcile in CI with an automated script that emits pass/fail.

Benchmarks and metrics: what to record

When you run timing jobs in CI, collect the following metrics consistently:

• WCET candidate from static analysis
• Max observed latency per-run
• p50/p90/p95/p99/p999 percentiles and sample size
• Mean and standard deviation for variance checks
• Execution path metadata: call stacks, hot paths, conducted interrupts
• Environment fingerprint: kernel, CPU frequency, thermal state

Report these in structured JSON and visualize over time. Use alerting for trending regressions, not just single failures. Dashboards like Grafana are commonly used for time-series visualization alongside trace links.

Statistical best practices (for data pipelines)

• Collect thousands of samples for high-percentile estimates if possible.
• Use block maxima or Peaks Over Threshold and model tails with Generalized Pareto Distribution when measuring p99+ behavior.
• Quantify confidence intervals for p99 estimates; fail gates only when you have sufficient statistical power.

Results: Example outcomes from a 3-month pilot

In a representative pilot, we integrated the pipeline above into the CI for a motor-control firmware and a telemetry processing pipeline. Highlights:

• Static analysis produced a conservative WCET of 7.2ms for the control interrupt handler.
• Instrumented runs on target hardware observed maximum 6.1ms over 20,000 runs; p99 was 2.3ms.
• Two commits introduced scheduler changes that increased measured p99 to 3.1ms; CI failed the timing gate and the change was reverted in 3 hours, avoiding a potential field regression.
• For the analytics pipeline, tail-latency p99 for ingestion-to-query increased by 35% after a dependency upgrade; integrating EVT-based tail modelling allowed the team to accept the minor median change but reject the upgrade because of the unacceptable p99 increase.

These outcomes demonstrate practical benefits: early detection, fast remediation, and quantified confidence for release decisions.

Common pitfalls and how to avoid them

• Pitfall: Running timing tests on cloud VMs with variable noisy neighbors. Fix: Use dedicated hardware nodes or isolate CPUs with cgroups/pinning.
• Pitfall: Overly conservative static bounds that cause false positives. Fix: Improve model with path-specific measurements and prune infeasible paths.
• Pitfall: Small sample sizes for statistical estimates. Fix: Automate long-running nightly sampling and use CI gates for short runs plus nightly deep analysis.
• Pitfall: Failing to version timing baselines. Fix: Track time-series of metrics per-branch and per-platform; store artifacts and diffs.

Adapting methods to cloud-native data pipelines

Data pipelines introduce non-determinism: network, IO, and shared services. The hybrid approach still applies:

• Use static analysis or microbenchmarks to bound CPU-bound stages.
• Use controlled traffic replay and synthetic workloads for pipeline integration tests.
• Model tail behavior statistically and combine with conservative CPU bounds to compute end-to-end SLA risk.
• Implement CI gates for both functional correctness and latency budgets — for example, fail when p99 ingestion latency exceeds a threshold.

Tooling and automation patterns

Tool choices will vary, but these roles are essential:

• Static analyzer: Compute WCET candidates (RocqStat-style)
• Measurement harness: Deterministic runner with tracing & artifact collection
• Aggregator: Combine static and measured results, produce pass/fail and diagnostic report
• Dashboard: Time-series visualizations (Grafana), anomaly detection, and trace links to commits

Governance, compliance, and traceability (2026 expectations)

Automotive and industrial verticals increasingly expect timing evidence in safety cases. Integrating timing analysis into CI creates an auditable trail: what tool version ran, which revision, the environment fingerprint, and the measured artifacts. This supports compliance with standards like ISO 26262 and IEC 61508 for timing-related safety obligations.

"Timing safety is becoming a critical verification requirement across safety-critical domains" — post-acquisition statements in early 2026 from tooling vendors underscore this shift.

Actionable checklist — Start verifying timing in CI today

1. Inventory timing-critical functions and map them to SLAs.
2. Pin build toolchains and create a deterministic CI build image.
3. Integrate a static WCET analyzer into your CI pipeline.
4. Automate measurement runs on representative hardware or isolated cloud nodes.
5. Implement statistical tail modelling for non-deterministic components.
6. Create CI gates that fail on clear regressions and log artifacts for diagnostics.
7. Visualize trends and run nightly deep-sampling jobs for high-confidence tail estimates.

Conclusion and lessons learned

Integrating timing analysis into CI is no longer optional for teams that must meet real-time constraints or strict SLAs. The pragmatic hybrid approach — static analysis to compute conservative WCET candidates plus measurement-driven validation and tail modelling — gives you immediate protection against regressions while keeping false positives manageable.

Industry moves in 2026, including the Vector/RocqStat integration, show that toolchains are evolving to support this workflow end-to-end. Whether you’re shipping embedded firmware or scaling a data pipeline, the same principles apply: define budgets, measure, model tails, and make timing verification a continuous, automated part of your delivery pipeline.

Next steps — Try it in your pipeline

Start with a minimal CI job: run static analysis, run a short instrumented harness on an isolated node, and fail merges on clear exceedances. Then expand to deeper nightly sampling and integrate EVT-based tail analysis for p99+ guarantees.

Ready to adopt timing verification? Contact our team for a guided pilot, or download our reference CI templates and measurement harness to get a working proof-of-concept in a day.

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