Integrating WCET and Timing Analysis into Your CI: A Step-by-Step Guide

Hook — Stop timing regressions from reaching your ECU

If your team manages embedded or automotive software, you know the pain: a merged change that looks harmless on functional tests suddenly causes missed deadlines on the road. Timing regressions are subtle, expensive to diagnose, and deadly for safety arguments. The solution is simple in concept and complex in practice: shift timing analysis left by incorporating WCET and timing tools into your CI pipeline so regressions are detected before firmware hits hardware or vehicles.

Why integrate WCET/timing analysis into CI in 2026?

Two trends make CI integration urgent in 2026. First, software content and complexity in vehicles continue to rise—ADAS, electrification, and zonal architectures increase timing-critical paths. Second, tooling is converging: Vector Informatik's January 2026 acquisition of RocqStat signals a coming wave of unified verification and timing toolchains that can be automated in CI. Embedding timing checks into CI gives teams predictable cost and faster certification evidence generation.

"Timing safety is becoming a critical ..." — Eric Barton, Vector Informatik (on the RocqStat acquisition)

High-level approach

At a glance, you need a reproducible way to produce the inputs required by timing tools, run the timing analysis deterministically in CI, compare results to baselines, and enforce policies. The pattern below works for static WCET tools (e.g., RocqStat) and dynamic/measurement-based tools when you have HIL or trace capture:

1. Baseline — establish a trusted WCET baseline per target and critical function.
2. Automate — wrap timing analyses in CLI tasks within your CI (GitHub Actions, GitLab CI, Jenkins, etc.).
3. Compare — diff results and detect regressions beyond configurable thresholds.
4. Fail fast — block merges or flag PRs when regressions exceed your safety margin.
5. Track — persist results to enable trend analysis and audits for safety standards like ISO 26262.

Prerequisites — what must be in place

• Deterministic build reproducibility: same compiler, flags, linker maps, and build environment tracked in CI.
• Toolchain access: CLI access to VectorCAST and RocqStat (or equivalent), license server config, and container images for reproducibility.
• Target model: CPU timing model or hardware-in-the-loop configuration for measurement-based analysis.
• Baseline artifacts: reference WCET reports and a baseline commit id.
• Policy: thresholds and decision rules (e.g., fail PR if WCET increase > 5%).

Step-by-step: Integrating RocqStat/VectorCAST into CI

1 — Create a reproducible timing job

Package the timing tool in a container with the exact tool versions, compilers, and license configuration. Example Dockerfile sketch:

FROM ubuntu:22.04
ENV LICENSE_SERVER="27000@licenseserver.company"
COPY vectorcast /opt/vectorcast
COPY rocqstat /opt/rocqstat
ENV PATH="/opt/vectorcast/bin:/opt/rocqstat/bin:$PATH"

Store this image in your registry and reference it in CI. This ensures the same binaries run in developer machines, CI, and any analysis agents.

2 — Baseline WCET per branch or target

On a stable branch (release/master), run a full WCET analysis and store the result as your baseline artifact. The baseline should include:

• WCET report (XML/JSON/PDF)
• Binary map and symbol table
• Tool configuration and CPU model
• Commit id and build metadata

Persist these to artifact storage (S3, Artifactory) with versioning.

3 — Add a CI job to run timing on PRs

Run a lightweight but deterministic analysis on every PR. Use incremental analysis if supported: analyze changed objects or functions only. Example GitHub Actions job (simplified):

name: Timing Analysis
on: [pull_request]
jobs:
  wcet:
    runs-on: ubuntu-latest
    container:
      image: myregistry/rocq-vector:2026.01
    steps:
      - uses: actions/checkout@v4
      - name: Build
        run: make CROSS_COMPILE=arm-none-eabi-
      - name: Run VectorCAST unit tests
        run: vectorcast run --project my_ecu
      - name: Run RocqStat WCET
        run: |
          rocqstat analyze --map build/my_ecu.map --output results/wcet.json
      - name: Compare vs baseline
        run: python ci_scripts/compare_wcet.py results/wcet.json baselines/master/wcet.json

The compare step should emit a non-zero exit code when thresholds are exceeded, causing the PR to fail fast.

4 — Implement regression detection and thresholds

Create a decision file that captures your acceptance policy. Example JSON:

{
  "global_threshold_pct": 5,
  "per_function_thresholds": {
    "SensorReadTask": 10,
    "ControlLoop": 2
  },
  "fail_on_any_violation": true
}

The CI comparison script should:

• Parse rocqstat output to extract WCET by function
• Compare each value to the baseline
• Respect function-specific thresholds for known noisy functions
• Return a non-zero exit code if violations exceed policy

5 — Store artifacts and publish metrics

Persist WCET reports and expose metrics for trend visualization. Recommended metrics:

• WCET per critical function
• Max WCET per build
• Number of regression violations

Implement a bridge that converts WCET JSON to Prometheus metrics or sends to an observability backend (InfluxDB, Timescale). This enables dashboards and alerts when trends indicate creeping regression even if each PR passes.

Triage and developer workflow

Fast, actionable feedback is the goal. When a PR fails timing checks, automate these steps:

• Post a detailed CI comment with top 5 functions showing regression and delta (absolute and percent).
• Include links to artifacts and diff view to speed root-cause analysis.
• Provide suggested mitigations: compiler flags, function-inlining changes, or algorithmic fixes.
• If the regression is spurious (tooling noise), provide an escape hatch: a documented reviewer override with justification and traceability.

Handling measurement variability and flakiness

Timing analysis has two flavors: static WCET that reasons about paths and microarchitectural effects, and measurement-based that depends on captured execution traces. Both require strategies to reduce noise:

• Reproducible builds: Always run timing on identical binaries by using immutable build artifacts cached in CI.
• Repeat runs: For measurement-based analysis, run N iterations and use the maximum, or use statistical methods to estimate the 99.999 percentile for safety margins. Consider modern AI-assisted path selection concepts to prioritize expensive analysis runs.
• Controlled environment: Execute on dedicated CI agents or HIL racks isolated from background noise. If using cloud, use instances that permit real-time scheduling.
• Noise budgeting: Allocate and tune a noise budget in your thresholds for functions known to be I/O-bound or scheduler-sensitive.

Advanced strategies for automotive multicore systems

WCET for multicore and mixed-criticality systems is still an active research and industrial area. Practical strategies:

• Compositional timing analysis: Analyze components individually and use interface WCETs plus scheduling analysis to compute system-level bounds.
• Use timing-aware partitioning: Run timing-critical tasks on isolated cores or use cache partitioning to reduce interference.
• Hybrid approach: Combine static WCET for core control code and measurement for less critical or I/O-heavy tasks.
• Schedule simulation: Integrate timing outputs into your scheduling model (e.g., SymTA/S or Cheddar) to validate end-to-end latency.

Case study: catching a 7% WCET regression before release

Context: A mid-size OEM team maintains an ABS ECU. They integrated VectorCAST + RocqStat into CI in Q2 2026. During a PR, a seemingly local refactor replaced a hand-rolled circular buffer with std::deque for convenience. The PR passed functional unit tests but failed the timing check—WCET for the BrakeControlLoop increased by 7%.

Outcome:

• CI failed the PR with a generated comment showing the function-level delta and binary diff link.
• Developer reverted the container change, replaced std::deque with a bounded ring buffer, and reran CI. WCET returned to baseline.
• The recorded artifact and PR trail were used as evidence during the ISO 26262 safety case.

Key lesson: timing checks found a performance regression that functional tests missed. The cost of fixing in CI was minutes; the cost if shipped could have been months in field recalls and certification rework.

Toolchain and licensing considerations

• License servers: CI agents must access license servers—either via VPN or license proxies. Use lease-based licenses and short expirations for CI to avoid exhaustion.
• Containerized licenses: Some vendors support license-in-container approaches; gating access to the image registry and secrets management is critical.
• Offline CI: If build agents are air-gapped for security, set up a local license server or vendor-supported offline activation for the analysis tools.
• Security: Treat tool configuration and CPU model files as deliverables of your safety case—track changes in SCM and sign artifacts.

Observability: make WCET a first-class metric

Do not treat WCET reports as static documents. Instead:

• Expose them as time-series metrics (max WCET per build, per function).
• Create alerting rules for trending increases.
• Automate periodic full analysis (nightly) to catch slow regressions unobserved in PRs.

2026 trends and future predictions

Recent vendor consolidation and demand for integrated verification pipelines are shaping 2026 toolchains. The acquisition of RocqStat by Vector is an example: expect tighter integration between WCET engines and test frameworks (VectorCAST) that exposes programmatic APIs for CI automation. Additional trends to plan for:

• Unified APIs: Toolsets will provide REST/CLI contracts making CI integration repeatable across projects.
• Cloud-hosted timing services: Expect secure, certified cloud offerings for timing analysis that reduce on-premise license complexity.
• AI-assisted path selection: Machine learning will prioritize hot or risky paths to reduce analysis time while still finding regressions.
• Standardized artifacts: Industry workgroups will push standardized WCET report formats (JSON schemas) to simplify dashboards and evidence collection.

Checklist: Quick implementation plan

1. Containerize timing tools and pin versions.
2. Establish a baseline WCET and save artifacts in immutable storage.
3. Add a CI job to run timing analysis on PRs; fail on policy violations.
4. Store results in a metrics backend for trend dashboards and alerts.
5. Automate PR comments with actionable diffs and links to artifacts.
6. Set up license management for CI and HIL agents.
7. Document the process and include outputs in your safety case artifacts.

Actionable takeaways

• Shift left. Integrate WCET and timing analysis into PR-level CI to catch regressions early.
• Baseline and compare. Use deterministic baselines and automated diffing with thresholds to avoid noisy failures.
• Automate traceability. Persist WCET artifacts for audits and safety evidence.
• Plan for multicore. Use compositional analysis and partitioning to manage complexity.

Next steps — a recommended pilot

Pick one small safety-critical module and run a 4-week pilot: containerize the toolchain, baseline WCET, and add a PR-level CI check. Measure detection time, false positives, and developer overhead. Use those metrics to refine thresholds and policies before scaling to the full product line.

Call to action

If you need help setting up a reproducible CI pipeline for WCET and timing analysis—especially integrating VectorCAST and RocqStat—numberone.cloud provides hands-on workshops, toolchain integration services, and CI pipeline templates tuned for automotive requirements. Contact us to schedule a 1-day workshop and get a pilot pipeline that blocks timing regressions before they reach your fielded ECUs.

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