WCET and CI/CD: Automating Timing Analysis with VectorCAST + RocqStat

Hook: Stop Late-Stage Surprises — Automate WCET (Worst-Case Execution Time) into CI/CD pipelines

One late build, one missed scheduling path, and suddenly your safety case has a timing gap. For teams building safety-critical embedded systems in 2026, that scenario is no longer acceptable. With software complexity rising across automotive, avionics and industrial control systems, you need WCET (Worst-Case Execution Time) analysis as an automated gate in your CI/CD pipelines — not a manual verification step performed weeks before certification.

The evolution in 2026: Why WCET Belongs in CI/CD Now

Late 2025 and early 2026 brought a visible shift in tool consolidation and verification workflows. Vector Informatik's January 2026 acquisition of StatInf's RocqStat and its announced plan to integrate RocqStat into VectorCAST is concrete evidence: vendors are unifying timing analysis and software testing into a single toolchain to meet growing demand for timing safety across software-defined vehicles and other real-time domains. (Source: Automotive World, Jan 16, 2026.)

This consolidation matters because modern verification is not just about functional correctness — it's about predictable timing under mixed-criticality workloads, multi-core scheduling, and complex microarchitectural effects. Toolchains that expose WCET data directly into CI systems let teams detect regressions early, enforce timing budgets, and provide artefacts for safety standards such as ISO 26262 and DO-178C.

How VectorCAST + RocqStat changes the game

VectorCAST is established as a code testing and verification environment; RocqStat brings advanced timing analysis and WCET estimation expertise. Their integration creates a workflow where unit tests, integration tests and timing analysis are part of the same verification story. Practically, that enables:

• Unified reports: Correlate failing timing budgets with failing tests and code coverage in one place.
• Automated gating: Fail merges or releases when WCET exceeds a defined threshold or regresses beyond an allowed delta.
• Reproducible artefacts: Store WCET reports per commit for traceability during audits and safety cases.

Core principles for WCET in CI/CD

Before we get to pipelines and scripts, align on these design principles:

• Determinism: Use locked tool versions, deterministic compilers, and containerized runners so WCET runs are reproducible.
• Hybrid verification: Combine static WCET estimation (path-sensitive) with measurement-based validation on representative hardware.
• Fail fast, informed: Fail CI on timing regressions but provide actionable diffs and hot paths so developers can remediate quickly.
• Traceability: Store WCET artefacts alongside code coverage, unit tests and requirements for audits.

Practical pipeline design: stages and gating

Below is a recommended CI pipeline structure that integrates VectorCAST and RocqStat-based timing analysis. Replace stage names and tool invocations for your CI product (GitLab CI, Jenkins, GitHub Actions, Azure Pipelines).

Suggested stages

1. Checkout & build: Clean build with locked toolchain and deterministic flags.
2. Unit & integration tests: Run VectorCAST tests and coverage instrumentation.
3. Static WCET analysis: Run RocqStat/analysis to produce WCET per unit/module.
4. HW measurement validation: (Optional stage) Run microbenchmarks on representative HW runners or HIL systems.
5. Report & gate: Aggregate results, compare against baselines, break the pipeline if budgets are violated.
6. Archive artefacts: Store WCET reports, coverage data and diff reports for traceability.

Sample GitLab CI YAML (conceptual)

# .gitlab-ci.yml - conceptual
stages:
  - build
  - test
  - wcet
  - validate
  - publish

build:
  stage: build
  image: registry.myco.com/vectorcast:2026.1
  script:
    - ./scripts/clean.sh
    - ./scripts/build.sh --toolchain=arm-none-eabi-10 --deterministic
  artifacts:
    paths: [build/]

unit-test:
  stage: test
  image: registry.myco.com/vectorcast:2026.1
  script:
    - vectorcast run --project tests/project.vcp
    - vectorcast export-coverage --output reports/coverage.xml
  artifacts:
    paths: [reports/]

wcet-analysis:
  stage: wcet
  image: registry.myco.com/rocqstat:2026.1
  script:
    - rocqstat analyze --input build/output.elf --target-model models/target.json --out reports/wcet.json
    - python tools/wcet_delta.py --baseline artefacts/baseline/wcet.json --current reports/wcet.json --threshold 5
  artifacts:
    paths: [reports/wcet.json]

publish:
  stage: publish
  script:
    - ./scripts/publish_reports.sh reports/
  when: always

This example shows how to run a deterministic build, execute VectorCAST tests, then run RocqStat analysis and compare the WCET to a baseline. The pipeline fails in the wcet-analysis job if wcet_delta.py finds a regression beyond an allowed percentage.

Implementing reliable WCET checks: technical checklist

Use this checklist to avoid common pitfalls when adding WCET to CI/CD:

• Toolchain locking: Pin compilers, linkers and tool versions in the CI image. Small changes in code generation can change WCET significantly.
• Model the hardware: Provide RocqStat with accurate CPU models — cycles per instruction, cache parameters, pipeline, and bus timing. Inaccurate models lead to pessimistic or optimistic WCETs.
• Isolate scheduling effects: Run static analysis with task models and priorities so results reflect realistic preemption and scheduling.
• Path sensitivity: Ensure your analysis explores all feasible paths. Use VectorCAST coverage data to identify untested paths that may hide worst-case behavior.
• Baseline and delta analysis: Store a golden WCET baseline for your release branch, and compare new results to detect regressions early.
• Scaling & resource planning: Static timing analysis can be CPU- and memory-intensive — provision dedicated CI runners or use on-premise HPC resources for large codebases.

Handling nondeterminism: caches, interrupts and concurrency

Microarchitectural features (caches, branch predictors) and concurrency (RTOS, interrupts, multi-core) make WCET hard. RocqStat's algorithms are designed to reason about path and scheduling effects, and when integrated with VectorCAST you can connect test scenarios and coverage to timing paths. Still, follow these practical steps:

• Define a realistic task model: Specify priorities, periods and budget to the analyser. Modeling tasks gives a context for preemption and helps pin down feasible worst-case traces.
• Use locking or partitioning for critical paths: If an algorithm cannot tolerate cache-induced variability, consider cache locking or partitioning to reduce timing uncertainty.
• Combine static and measurement: Use HW measurements to validate analysis and to calibrate platform models used by RocqStat.

Actionable remediation patterns after a WCET regression

When CI flags a timing regression, the output needs to point developers to an actionable plan. Here are proven remediation patterns:

1. Hot-path isolation: Use the WCET report to identify the function(s) contributing most of the increase and add focused unit tests and path constraints to reproduce the path locally.
2. Micro-optimizations: Replace heavy library calls with deterministic implementations, reduce branching in hot loops, and prefer fixed-point math if floating point is unpredictable.
3. Compiler tuning: Try alternative optimization levels or flags that favor predictability over raw speed (e.g., -O2 vs -Ofast), and measure effects under the same toolchain.
4. Architectural fixes: If multi-core interference causes unacceptable WCET, consider task migration, core affinity, or partitioning critical tasks to dedicated cores.
5. Algorithmic redesign: If the worst-case path is algorithmic (e.g., pathological input), add checks that limit input size or add graceful degradation strategies to bound execution time.

Metrics and dashboards: what to track

Expose these metrics in your CI dashboards and safety artefact repositories:

• Absolute WCET per module/function (ms or cycles)
• Regression delta vs baseline (percentage and absolute)
• WCET coverage: Fraction of code paths exercised by tests used for WCET derivation
• Average and median execution times for performance monitoring
• Number of failing timing gates per branch/merge request

Case study (composite example): ECU control loop timing

Context: An automotive ECU runs a 1 ms control loop for an actuator. The safety budget reserves 600 µs for the control routine. After integrating RocqStat with VectorCAST in CI, the team did the following:

1. Built deterministic Docker images with VectorCAST 2026.x and RocqStat models for the target MCU.
2. Added a wcet-analysis stage to GitLab CI that produced function-level WCET reports per commit.
3. Established a baseline on the protected release branch and set a 5% regression threshold for merge requests.
4. Upon a regression, the CI job attached a differential report showing the new hot path: a newly added library call that used dynamic memory and indirect branching.
5. Developers replaced that call with a deterministic implementation, and WCET returned below budget. The change and the WCET artefacts were archived for the safety assessment.

Outcome: Early detection saved multiple weeks of rework and provided traceable artefacts for the ISO 26262 safety case.

Operational tips for enterprise adoption

• Train developers: Developers need to interpret WCET reports; provide workshops pairing developers with verification engineers.
• Automate triage: Create automated reviewers or bots that annotate pull requests with WCET deltas and hot path links.
• Plan compute: Allocate dedicated runners for heavy analyses and schedule overnight runs for exhaustive analyses.
• Keep a safety branch: Run full, conservative WCET analyses on long-lived release branches and lightweight checks on feature branches.

"Timing safety is becoming a critical requirement across software-defined industries." — Vector Informatik, 2026

Looking forward: trends and predictions for WCET tooling (2026+)

Expect these developments to accelerate in 2026 and beyond:

• Deeper toolchain integration: More vendors will embed WCET analysis into verification suites (we already see this with Vector's RocqStat acquisition).
• Cloud-native verification: Scalable WCET analysis on cloud clusters with secure hardware models is becoming common for large codebases.
• AI-assisted triage: Machine learning to prioritize hot paths and suggest code changes that reduce WCET without hand-tuning.
• Standardization efforts: Tighter guidance from standards bodies on how timing evidence must be presented in safety cases.

Final checklist to ship WCET in CI this quarter

1. Define your timing budgets and baselines per task or function.
2. Containerize and lock toolchain images (VectorCAST + RocqStat versions).
3. Integrate static WCET analysis as a CI stage with delta gating against baseline.
4. Store and version WCET artefacts and link them to commits and MR/PRs.
5. Combine with measurement-based validation for final certification evidence.
6. Automate triage and educate developers on interpreting WCET reports.

Call to action

Start treating timing as code: add WCET checks to your CI/CD today and avoid late-stage surprises. If you want a practical kickoff, download our CI integration checklist and a sample pipeline (VectorCAST + RocqStat-ready), or contact our engineers to run a 2-week proof of concept on your codebase. Make timing verification an automated, reproducible part of your delivery pipeline — your certification timeline will thank you.

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