How VectorCAST + RocqStat Changes Automotive Dev Workflows: A Case Study

Hook: Timing problems silently derail automotive releases — here's how to stop them

Delivery dates slip, field incidents trace back to unexpected latencies, and teams scramble to re-test on target hardware: these are familiar failure modes for automotive software teams. The root cause is often missing timing verification early in the lifecycle. In 2026, with Vector's January 2026 acquisition of RocqStat into VectorCAST, automotive teams finally have a unified path to make timing—specifically WCET (worst-case execution time)—a first-class citizen in their verification and release gates.

Executive summary — what this case study shows

This hypothetical case study walks through how a mid-sized OEM-tier supplier (we'll call them "Horizon Controls") integrated RocqStat's timing analysis into an existing VectorCAST test-driven toolchain in early 2026. The integration changes developer workflows, test cadence, and release gates in measurable ways:

• Developers get earlier timing feedback via local and CI checks.
• Testing cadence shifts to a mix of fast incremental timing probes and nightly full WCET runs on representative targets.
• Release gates expand from functional coverage and MISRA to include WCET margins and timing regressions.
• Incident response and triage become faster because timing artifacts (call graphs, path traces) are linked directly to tickets.

Context: Why timing analysis matters in 2026

Late 2025 and early 2026 saw two converging trends: software-defined vehicles drove exponentially more code into safety-critical ECUs, and regulators and OEMs increasingly demanded demonstrable timing safety. Vector's January 2026 acquisition of RocqStat (the timing-analysis team formerly at StatInf) is a direct industry response: combining timing analysis with unit and integration testing creates a single verification story for both functional and temporal correctness.

WCET is no longer a niche deliverable—it's a release criterion. For multicore systems, mixed-criticality features, and adaptive applications (over-the-air updates), you must show not just that code behaves correctly but that it completes within defined budgets under worst-case conditions.

Horizon Controls: starting point

Horizon Controls maintains a widely used ECU platform running AUTOSAR Classic, with a CI pipeline based on Jenkins and VectorCAST for unit and integration tests. They already track code coverage, MISRA violations, and functional regression windows. Timing analysis was ad hoc: a handful of engineers ran manual measurement campaigns on target hardware near milestones.

Pain points:

• Timing regressions discovered late in integration, often after a hardware-in-the-loop (HIL) bench cycle.
• Long lead times to reproduce target hardware test results locally.
• Release gates lacked objective timing metrics; delays were handled manually and inconsistently.

Integration plan — phased and pragmatic

Horizon adopted a three-phase integration strategy to minimize disruption and quickly deliver value.

Phase 1 — Pilot and toolchain validation (4 weeks)

• Install RocqStat integration into VectorCAST on a dedicated CI node that can access the hardware-in-the-loop (HIL) bench and an instruction-accurate simulator.
• Select two candidate modules with known timing sensitivity (scheduler and comm stack) for pilot WCET analysis.
• Define baseline WCETs using RocqStat on target and simulator; collect call graphs and path lists.

Phase 2 — Developer feedback loop (6–8 weeks)

• Embed incremental timing checks in developer workflows: pre-merge jobs run a fast (approximate) timing probe and static heuristics; merge triggers a CI job that runs RocqStat in a delta mode.
• Provide local scripts that use an instruction-level simulator to give sub-minute WCET estimates for small functions during dev cycles.
• Run nightly full WCET estimation for the entire subsystem on the HIL bench.

Phase 3 — Gate enforcement and metrics (4 weeks)

• Create release gates that combine functional coverage, MISRA, and timing metrics.
• Automate ticket creation for any function exceeding a defined WCET threshold.
• Roll out training for developers and reviewers to interpret RocqStat reports and act on timing hotspots.

Actionable: CI pipeline examples and gating policy

Below are practical artifacts Horizon used. Replace command names and endpoints with your actual environment.

Sample GitHub Actions step to run a fast timing probe

name: Fast timing probe
on: [pull_request]
jobs:
  timing-probe:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Build unit tests
        run: make -j4 build-tests
      - name: Run VectorCAST quick test + RocqStat probe
        run: |
          vectorcast-cli run --tests quick-suite --report fast-report.xml
          rocqstat-cli probe --input fast-report.xml --mode approximate --output wcet-probe.json
      - name: Fail on quick timing regression
        run: |
          python tools/check_wcet_regression.py wcet-probe.json --threshold 5

That approximate probe gives developers immediate feedback; it intentionally favors speed over completeness. Full WCET runs are still performed nightly — teams balance cost and speed by tracking CI resource allocation and cloud spend (see notes on cloud cost controls).

Jenkins pipeline snippet for nightly full WCET

pipeline {
  agent { label 'hil-bench' }
  stages {
    stage('Checkout') { steps { checkout scm } }
    stage('Build') { steps { sh 'make -j8' } }
    stage('VectorCAST full') { steps { sh 'vectorcast-cli run --suite full --report full.xml' } }
    stage('RocqStat WCET') { steps { sh 'rocqstat-cli run --input full.xml --target-board kannen --output wcet-full.json' } }
    stage('Publish') { steps { archiveArtifacts artifacts: 'wcet-full.json', fingerprint: true } }
  }
  post {
    always {
      sh 'python tools/assign_wcet_issues.py wcet-full.json'
    }
  }
}

Concrete release gate: timing metrics

• WCET budget compliance: 0 functions may exceed the assigned execution budget for the release (hard fail).
• Timing regression: Average WCET increase must be < 5% vs baseline (soft fail — requires justification).
• Hotspots: No more than 3 functions per subsystem in the top-10 risk list (mitigation plan required).

How workflows change — developer, CI, and release manager perspectives

Developer

• Local builds include a fast timing probe; code review now includes a timing report snippet for modified functions.
• When a timing hotspot appears, the developer receives a linked ticket with a RocqStat call graph and recommended mitigations (e.g., refactor loop, reduce worst-case branching).
• Developers adopt microbenchmarks and use instruction-accurate simulation to validate patches before pushing.

CI / Test engineer

• CI now has two tiers of timing: fast gates for pull requests and full WCET nightly suites. Engineers tune resource allocation to manage expensive WCET runs and instrument low-latency telemetry (see edge observability) for better scheduling.
• Test engineers maintain a mapping between functional tests and timing-critical paths to prioritize schedules on HIL benches.

Release manager

• Release dashboards include timing KPIs: percentage of functions within budget, number of timing regressions, mean WCET delta.
• Decisions to ship are now data-driven and auditable: WCET artifacts are archived alongside test reports and requirements traceability.

Incident response and debugging: faster root cause with timing artifacts

Before integrating RocqStat, Horizon often had to reproduce problems on HIL or in the lab and manually trace long-running execution. After integration, each failing timing check produced:

• A prioritized list of worst-case execution paths
• Per-path timing counterexamples that can be replayed on the simulator
• Automated links from the CI job to Jira with attached call graphs

That changed the triage workflow:

1. Assign the ticket to the author of the change that introduced the regression (CI identifies the commit range).
2. Reproduce locally using the RocqStat-provided input trace on the simulator (minutes, not hours) — teams found it helpful to run local reproductions on compact rigs similar to a Raspberry Pi-based setup for fast turnaround.
3. Measure the fix on the nightly HIL job; close the ticket with WCET evidence.

Practical mitigations for common timing hotspots

RocqStat reports frequently point to a handful of patterns. Here are proven mitigations Horizon applied:

• Unbounded loops with rare exits: add explicit loop bounds or rework into state machine with explicit time budgets.
• Deep recursion: replace with iterative patterns or ensure bounded depth via static checking.
• Expensive library calls: isolate and wrap with caching layers for best/worst case difference reduction.
• Preemption-sensitive regions on multicore: use appropriate locking and avoid scheduling-critical blocking calls within high-priority tasks; integrate scheduling analysis into design reviews.

Metrics to track adoption and ROI

Horizon tracked these KPIs to quantify impact:

• Pre-merge WCET regressions caught: percent of timing issues discovered before integration vs prior baseline.
• Mean time to remediation (MTTR) for timing defects.
• Number of HIL re-runs avoided because issues were fixed earlier.
• Release delay days avoided due to earlier detection.

After three months, Horizon reported a 70% increase in timing issues found pre-merge and a 40% reduction in HIL re-runs during release candidate cycles — concrete savings that justified the pilot.

Organizational changes and training

Integrating timing analysis isn't only technical; it requires role and process shifts:

• Create a small timing center of excellence (1–2 engineers per product line) to coach teams and maintain toolchain scripts.
• Introduce a timing checklist into code reviews and design docs.
• Run workshops on interpreting WCET reports and converting them into testable hypotheses; teams often augment workshops with AI-driven brief templates to speed report summarization.

2026 trends and future predictions

As of 2026, expect these developments to continue shaping automotive toolchains:

• Vendors will further integrate functional and temporal verification tools into unified dashboards, making timing a non-optional gate.
• Multicore and heterogenous SoCs will force tighter coupling between scheduling analysis and WCET; tools will expose APIs for schedulability suites.
• Regulatory guidance and OEM expectations will increasingly require archived timing evidence for ASIL/SOTIF artifacts.
• Machine learning will assist test prioritization: ML models trained on historical timing violations will suggest critical tests and code paths to analyze first.

Vector's acquisition of RocqStat is symptomatic: toolchains are converging to provide a single source of truth for verification artifacts.

Key takeaway: Timing analysis integrated into the test toolchain shifts timing verification left — turning expensive late-cycle surprises into early, actionable developer feedback.

Checklist: How to get started (practical next steps)

1. Pick a pilot subsystem with known timing sensitivity.
2. Install RocqStat integration into your VectorCAST environment on a simulator and one HIL bench.
3. Define baseline WCETs and a conservative release gate for the pilot (hard fail for budget overrun).
4. Add a fast timing probe to pull-request jobs for immediate feedback.
5. Archive WCET reports and link them to tickets for traceability.
6. Measure KPIs (pre-merge catches, HIL re-runs, MTTR) and iterate.

Final practical example: interpreting a RocqStat call graph

When RocqStat highlights function handleMessage() as the top WCET contributor, follow this sequence:

• Open the call graph and identify the longest path (e.g., parse() -> decode() -> dispatch()).
• Inspect source-level hot spots in decode() (branches, loops, library calls).
• Create a microbenchmark for decode() and run it on the simulator to measure variance between average and worst-case.
• Implement mitigation (e.g., switch to table-driven decoder) and re-run quick probes until the hotspot drops below the budget threshold.

Conclusion and call-to-action

By integrating timing analysis (RocqStat) into an existing verification and testing flow (VectorCAST), automotive teams can turn timing from a late-stage risk into an early, measurable quality attribute. The changes are practical: updated CI jobs, new developer habits, and stronger release gates—but the payoff is fewer surprises, faster triage, and auditable evidence for safety reviews.

If you're responsible for ECU software verification in 2026, start a focused pilot this quarter: pick a timing-sensitive module, run an initial RocqStat baseline, and add a fast probe to your pull-request pipeline. The sooner timing becomes part of everyday feedback, the fewer late-cycle crises you'll face.

Ready to evaluate a unified timing + testing workflow? Start a pilot, archive your first WCET reports, and compare release KPIs after one milestone. For teams using VectorCAST, ask your toolchain operator about the RocqStat integration and plan a two-month pilot with one HIL bench—it's the fastest way to make timing a predictable part of your release process.