Automated Safety Evidence: Integrating Static Timing Analysis into Release Gates

Stop shipping timing regressions: automatically block releases on WCET spikes

Short version: Integrate static timing analysis outputs (RocqStat/VectorCAST) into your CI/CD release gates so builds with WCET regressions are automatically failed, an issue is created in your tracker, and the release is blocked until engineering triages and fixes the root cause.

Timing regressions in safety‑critical code are expensive and dangerous. In 2026, with more software-defined vehicles, avionics and industrial systems, teams must treat timing analysis as a first-class quality gate rather than an optional verification step. Vector's January 2026 acquisition of StatInf's RocqStat signals a clear market move: timing analysis is moving inside mainstream verification pipelines (Automotive World, Jan 16, 2026).

“Vector will integrate RocqStat into its VectorCAST toolchain to unify timing analysis and software verification” — Automotive World, Jan 16, 2026.

Why a WCET-based quality gate matters in 2026

Traditional CI runs unit tests and functional checks but rarely enforces worst-case execution time (WCET) budgets. As architectures move to multicore, mixed‑criticality execution and more aggressive optimizations, timing margins can disappear silently between commits. A single compiler flag or loop change can push an ISR or deadline-bound path over its safe limit.

Integrating timing analysis into your release gates gives you:

• Automated safety enforcement — CI blocks merges/releases on regressions, preventing shipping unsafe code.
• Faster triage — automated issues contain WCET deltas, stack traces and artifacts so developers reproduce and fix faster.
• Auditability — every gating decision is recorded for compliance (ISO 26262, DO-178C) and post‑release reviews.

High-level architecture: how it works

At a glance, the integration is a pipeline that runs timing analysis, compares results against a baseline, and enforces a release decision. Keep the flow simple and deterministic.

Data flow

1. CI builds the artifact (binary or instrumented image).
2. Run VectorCAST/RocqStat timing analysis (native container or dedicated runner/HIL).
3. Produce structured results (XML/JSON) with per‑path WCET numbers and metadata.
4. Analyzer compares WCET outputs to a trusted baseline and computes deltas and confidence.
5. If any delta exceeds policy thresholds, the analyzer fails the CI quality gate, creates an issue in the issue tracker, and attaches artifacts (logs, diffs, reproduction steps).
6. Release tooling prevents promotion until the issue is resolved and the gate passes.

Implementation patterns — step-by-step

Below are concrete recipes to implement automation. Adapt them to your CI platform, security policies and hardware availability.

1. Running RocqStat/VectorCAST in CI

Options depend on whether you can run static timing analysis purely offline (static WCET estimation) or need measurement-based estimation with target execution.

• Containerized Runner: Package VectorCAST and RocqStat in a secure container with licensed keys. Use a dedicated runner (GitHub Actions self-hosted, GitLab runner, Jenkins agent) that has access to the right toolchains.
• Hardware-in-the-loop (HIL): For measurement-driven timing, trigger automated test benches via the CI runner and collect results back to the pipeline.
• Hybrid: Run static ROCQ analysis in CI and schedule periodic HIL runs for validation.

2. Produce structured results

Make sure the tool emits results in machine‑readable form (XML/JSON). VectorCAST and RocqStat both support structured exports; if the built-in format is XML, convert to JSON if that simplifies comparison.

3. Compare WCET outputs and detect regressions

Create a small analyzer service (Python/Go) that:

• Parses the timing result file.
• Maps results to known safety tasks/functions using unique IDs.
• Compares each path WCET to a stored baseline (artifact from last green release or a certified baseline branch).
• Computes absolute and percentage deltas and an uncertainty metric (for measurement noise).
• Decides pass/fail using policy rules (see threshold section).

Example Python pseudo-code (simplified):

from xml.etree import ElementTree as ET

NEW = ET.parse('rocq_results.xml')
BASE = ET.parse('baseline_results.xml')

for func in NEW.findall('.//function'):
    id = func.find('id').text
    new_wcet = float(func.find('wcet').text)
    base_wcet = float(BASE.find(f".//function[id='{id}']/wcet").text)
    delta = new_wcet - base_wcet
    pct = 100.0 * delta / base_wcet
    if pct > POLICY_PCT or delta > POLICY_ABS:
        report_regression(id, base_wcet, new_wcet, delta, pct)
  

4. Define your quality gate policy

Policies must be pragmatic and reflect safety needs. Consider:

• Critical tasks: functions that participate in deadlines — fail on any increase > 0ms.
• Non-critical tasks: allow small percentage increases (e.g., 3–5%) unless absolute time crosses a budget.
• Measurement noise tolerance: apply statistical smoothing (3-run median) for measurement-driven data.
• Baseline selection: baseline from the latest certified release branch or a rolling window of N green builds.

Example policy (conservative):

• Fail if any critical path WCET delta > 0ms.
• Fail if any non‑critical path increases > 5% AND > 0.5ms absolute.
• Ignore deltas below 0.2ms when measurement variance > 10%.

5. Fail the pipeline and block the release

Most CI systems allow a job to fail and mark the build status. Use platform features to enforce required checks on protected branches or release pipelines.

GitHub Actions (example)

name: timing-analysis
on: [push]
jobs:
  wcet:
    runs-on: self-hosted
    steps:
      - uses: actions/checkout@v4
      - name: Run VectorCAST + RocqStat
        run: ./tools/run_timing.sh --output rocq_results.xml
      - name: Analyze WCET
        run: python tools/wcet_analyzer.py --new rocq_results.xml --baseline baseline.xml
  
# If wcet_analyzer.py exits non-zero, the Action fails and the required status check prevents merge
  

6. Create automated issues with context

When the analyzer finds a regression, create a ticket in your issue tracker with structured context so triage is fast. Include:

• Function ID/name, baseline WCET, new WCET, delta and percent.
• Stack trace or annotated assembly if available.
• Link to artifacts: full XML, compiler flags, build IDs and reproduction steps.
• Suggested owner rules (based on CODEOWNERS or static mapping).
• Priority based on safety impact and release schedule.

Sample GitHub issue body (JSON payload) or Jira REST payload can be generated by the analyzer and posted via API. Include pointers to CI logs and attach the rocq_results.xml for forensic work.

Practical examples: GitHub + VectorCAST integration

Here’s a compact, production-ready flow you can implement in a week if you have CI runners and tool licenses:

1. Containerize VectorCAST + RocqStat and publish to private registry.
2. Add a self-hosted GitHub Actions runner with access to the registry and licensing server.
3. Add a job that builds the image, runs unit tests, then executes timing analysis with a deterministic configuration.
4. Upload the XML/JSON results as an artifact and call analyzer service.
5. If analyzer fails, call GitHub Issues API with structured body and create a blocking label (e.g., wcet/regression).
6. Protect main and release branches to require the timing-analysis check for merge or release workflows.

Best practices to avoid false positives

• Stable baselines: Tie baseline artifacts to release tags or a protected baseline branch. Do not compare to arbitrary last commit when certifying.
• Noise reduction: For measurement-based WCET, run N repeats and use median; apply control charts to detect shifts.
• Scope gating: Apply the timing gate only to affected modules or safety-critical components to reduce CI cost and noise.
• Rerun on fail: Automatically queue a rerun on hardware or re-analyze with different seeds before creating a blocking issue.
• Traceability: Store full toolchain versions, compiler flags, and HW config with each result for reproducibility.

Operationalizing triage: closing the loop

An effective flow ties the analyzer to developer workflows:

• Auto‑assign ticket to CODEOWNERS for the failing function.
• Attach a reproduction guide: minimal test and command to re-run locally or on HIL.
• Provide quick mitigations: change config to adopt safer compiler flags or revert the suspected commit via cherry-pick.
• Once fixed, the analyzer runs again; the issue is resolved and the gate clears for release promotion.

Case study: ECU deadline enforcement (fictional, realistic)

Scenario: An ECU processing pipeline has an ISR path budget of 2.5ms. A change in an optimization flag pushes the measured WCET for the ISR to 2.7ms on nightly CI.

Before automation: regression slips into integration test, discovered late in HIL; costly debug and recall risk.

After automation:

• CI run flagged the WCET increase by +8% and exceeded the 2.5ms budget.
• The analyzer auto-created a high-priority ticket linked to the commit and attached rocq_results.xml and annotated assembly.
• Developer reverted compiler flag in a hotfix branch, analyzer re-ran and validated the ISR at 2.4ms. Release gate cleared.

Impact: prevented a costly late integration failure, shortened mean-time-to-detect from days to minutes, and provided an auditable trail for compliance.

Advanced strategies & future-proofing (2026+)

As tools converge (VectorCAST + RocqStat), expect richer integrations and analytics:

• Unified dashboards: VectorCAST integrations will provide time-series WCET analytics, enabling trend detection and ML-based anomaly alerts.
• Correlation across tools: Combine static timing data with coverage, fuzzing, and performance regressions for faster root cause analysis.
• SBOM & provenance: Sign WCET artifacts and tie them to SBOM entries to prove toolchain integrity for audits.
• Policy-as-code: Define timing quality gates declaratively (e.g., OPA) so gates are reproducible across CI platforms.

Checklist: deploy a timing-analysis quality gate in 30 days

1. Inventory safety-critical functions and map function IDs used by RocqStat/VectorCAST.
2. Provision CI runners with VectorCAST/RocqStat container and licensing access.
3. Implement timing analysis job and output structured results.
4. Build a simple analyzer to compare against a certified baseline and apply policy thresholds.
5. Integrate analyzer with your issue tracker (Jira/GitHub) to auto-create enriched tickets.
6. Protect branches and require the timing-analysis status check for merges/releases.
7. Run a pilot for one ECU or subsystem, iterate on thresholds and noise handling.

Common pitfalls

• Relying on floating baselines: lock baselines to a certified release tag.
• Overly strict thresholds: cause noise and alert fatigue—start conservative then tighten.
• Ignoring provenance: without toolchain metadata, results aren’t auditable for certification.

Actionable takeaways

• Make timing analysis a required quality gate for safety‑critical branches in CI/CD.
• Automate issue creation with WCET deltas, artifacts and owner assignment to speed triage.
• Use baselines and statistical smoothing to avoid false positives while catching real regressions early.
• Leverage VectorCAST + RocqStat integrations (now accelerated after the 2026 StatInf acquisition) to centralize verification workflows.

Next steps & call to action

If you run safety‑critical systems, don’t wait for a timing failure to force process change. Start by adding a timing-analysis job to your CI and wiring a basic analyzer that fails builds for any WCET overrun.

Need a blueprint or hands‑on help? Contact tunder.cloud to run a 2‑week pilot: we’ll implement a VectorCAST/RocqStat CI integration, set policy gates, and connect results to your issue tracker so your next release is safe by default.

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