WCET and Safety Pipelines: Best Practices for Continuous Timing Regression Monitoring

Stop Surprises in Production: Continuous WCET Monitoring for Safety Pipelines

Timing regressions in safety-critical software are not a nuisance, they are a liability. Teams building embedded systems for automotive, aerospace, and industrial control face strict requirements for determinism and bounded latency. Yet many CI pipelines still treat execution time as an afterthought. This article shows how to instrument, baseline, and alert on worst-case execution time regressions in CI using RocqStat and companion timing tools so your deployments stay safe, auditable, and auditable under pressure.

Why timing must be continuous in 2026

By 2026 the industry expectation is clear: timing safety is part of the CI contract. Recent developments emphasize this trend. In January 2026 Vector acquired RocqStat technology and team to embed statistical WCET methods into mainstream verification toolchains. That move is accelerating toolchain consolidation and making statistical WCET analysis part of continuous verification workflows. If you run safety critical fleets, you must do three things in CI: measure accurately, form defensible baselines, and respond to regressions automatically.

What safety teams are telling us

• They need repeatable timing measurements for certification artifacts and audits.
• They want statistically sound worst-case estimates instead of ad hoc max values.
• They require automated gates in CI that block releases when timing drift indicates higher risk.

Vector's acquisition of RocqStat signals a shift: statistical WCET is migrating from specialist labs into CI pipelines and mainstream verification tools.

Overview: The continuous timing regression workflow

Build a pipeline around five activities. Each stage has tactical steps you can implement today.

1. Instrument code paths and measurement harnesses
2. Collect deterministic samples under controlled conditions
3. Baseline WCET using statistical analysis (RocqStat, EVT, etc.)
4. Integrate checks into CI and gate on regressions
5. Alert and debug when regressions or drift occur

1. Instrumentation: get accurate, high-resolution measurements

Good timing starts with good signals. Aim to measure the actual execution path of interest with minimal overhead and noise.

What to instrument

• Entry and exit of critical functions or tasks that contribute to control loops.
• Interrupt handlers and ISR latency where applicable.
• Synchronization primitives that can introduce jitter (mutexes, semaphores).
• Scheduler boundaries and context switch hotspots.

How to instrument

• Prefer hardware cycle counters where available, for example DWT Cycle Counter on ARM Cortex-M, or CPU timestamp counters on x86. These measure cycles directly and are less prone to system timer jitter.
• Use clock_gettime with CLOCK_MONOTONIC_RAW for userspace timing if hardware counters are not accessible.
• Minimize instrumentation overhead. Keep probes tiny, avoid heavy logging inside hot paths.
• Support both sampling and tracing. Sampling gives low overhead; tracing (ETM, trace ports, LTTng) yields deterministic call sequences for root cause analysis.
• Provide a measurement adaptor that emits a simple, machine readable format (CSV or JSON with timestamp, event, thread id, cycles).

Example instrumentation pattern

static inline uint32_t read_cyclecounter(void) {
  // platform specific, pseudocode
  return DWT->CYCCNT;
}

void critical_work(void) {
  uint32_t start = read_cyclecounter();
  // ... work ...
  uint32_t end = read_cyclecounter();
  emit_measurement("critical_work", start, end);
}

2. Reliable data collection in CI and on hardware

Measurement environments must be controlled. CI often runs on virtualized builders where timing is non-deterministic, so plan for hardware-in-the-loop (HIL) or dedicated bare-metal runners for timing verification.

Key controls to reduce noise

• Disable CPU frequency scaling and turbo boost.
• Pin test workload to a dedicated core and isolate with cpusets or cgroups.
• Clear caches where relevant, or use cache warming consistently as part of the measurement protocol.
• Fix power and thermal conditions for the device under test where possible.
• Use identical firmware, bootloader, and runtime configuration between baseline runs and regression checks.

CI patterns

Embed a timing stage in your CI pipeline that runs on a fleet of dedicated test devices. Use the following pattern:

• Build and deploy firmware/artifact
• Run deterministic workload N times with fixed seeds
• Collect raw measurements and upload to artifact storage
• Run statistical analysis (RocqStat or similar)
• Compare with baseline and emit pass/fail

Example CI job (pseudocode YAML)

stages:
  - build
  - timing

build:
  stage: build
  script:
    - make all

timing_verify:
  stage: timing
  tags:
    - hardware
  script:
    - flash_device firmware.bin
    - run_timing_campaign --iterations 100 --output measurements.csv
    - upload measurements.csv
    - rocqstat analyze measurements.csv --out wcet_report.json
    - compare_baseline wcet_report.json baseline.json || exit 1

3. Baselining: produce defensible WCET estimates

A baseline is not a single number. It is a statistical model plus confidence bounds. Use both empirical sampling and Extreme Value Theory (EVT) for tail estimation. RocqStat and similar tools help turn repeated measurements into a WCET estimate with a specified violation probability.

Baseline campaign best practices

• Design the campaign to cover operational profiles and boundary cases.
• Run enough samples to reach the desired confidence. For high integrity levels you may need thousands of samples or EVT extrapolation.
• Record environment metadata: firmware git commit, compiler version, CPU microcode, boot arguments, tests seeds.
• Store raw data and the statistical model used to estimate the WCET for traceability.

Choosing confidence and violation probability

Safety standards and architectural risk assessments determine your required violation probability. For example, an automotive control loop may require a 1e-6 violation probability per activation. Translate this into a WCET estimate with an associated confidence level, and document the assumptions.

Using RocqStat for baseline

RocqStat applies statistical techniques tailored for timing tails. Use it to compute an estimate for a target exceedance probability and capture the model. A baseline artifact should include:

• WCET estimate and confidence interval
• Violation probability (alpha) used for extrapolation
• Raw sample count and campaign metadata
• Tool and version information (rocqstat version, date)

4. Regression detection and CI gates

Detecting regressions requires two capabilities: comparing a new WCET estimate against baseline and operationalizing alerts on statistical drift. Use both immediate hypothesis testing and longer-term control charts to catch slow degradation.

Fast gates: hypothesis testing on post-campaign estimates

After a CI timing run compute a WCET estimate from the new samples and test the null hypothesis that the new tail is not worse than baseline. If the test rejects the null at the chosen significance level, fail the build. For safety-critical systems prefer conservative alpha values and require reproducible failures.

Continuous drift detection: control charts and CUSUM

Not all regressions are abrupt. Use statistical process control techniques such as EWMA and CUSUM on periodic WCET estimates or on percentiles (p95, p99.9) to detect slow drift from thermal, compiler, or dependency changes.

Practical gating rules

• Hard stop: if estimated WCET at required violation probability exceeds baseline + margin, fail the pipeline.
• Soft alert: if estimate exceeds baseline but remains within safety margin, notify owner and create a ticket.
• Retest policy: require at least two independent failing campaigns before blocking a release to avoid false positives from flakiness.

Example compare script pseudocode

# compare_baseline script pseudocode
  new_wcet = parse wcet_report.json
  baseline_wcet = parse baseline.json
  safety_margin = baseline_wcet * 1.05  # 5 percent guard band

  if new_wcet > safety_margin:
    echo "WCET regression detected"
    exit 1
  else
    echo "Timing within baseline"
    exit 0
  fi

5. Alerts, triage, and root cause analysis

When CI flags a regression you need fast context and reproducible artifacts. The goal is to reduce mean time to resolution while preserving an audit trail.

What to capture on failure

• All raw measurement files and their metadata
• ETM or trace extracts for the failing campaign
• Compiler flags, linker map, binary diff between baseline and candidate
• Thermal and power telemetry if available
• Environment fingerprint: kernel, runtime, CPU microcode

Observability integration

Export per-commit WCET estimates to a time-series database such as Prometheus and visualize control charts in Grafana. Configure Alertmanager to create Slack or ticketing alerts only when conditions meet your triage policy. This reduces alert fatigue while ensuring traceability.

Advanced strategies and patterns

Beyond gating there are deployment patterns that reduce risk and surface regressions with minimal service impact.

Progressive rollout with timing canaries

• Deploy builds to a small canary fleet with enhanced timing telemetry enabled.
• Compare live timing against baseline and abort rollout if violations are observed.

Feature-flagged instrumentation

Enable detailed, higher-overhead instrumentation only for canaries or simulated environments. This keeps production overhead low while allowing deep analysis when needed.

Cross-variant baselines

Maintain separate baselines per hardware revision, compiler version, and RTOS configuration. Use automated baseline selection in CI based on DUT fingerprint to avoid false positives from expected platform differences.

Handling non-determinism and flakiness

Timing measurements are noisy by nature. Your CI must distinguish noise from real regressions.

Reduce noise first

• Lock environment variables and workloads.
• Restart DUT between runs to avoid stateful thermal drift.
• Record and compare median and high-percentile metrics, not single-run maxima.

Statistical controls

Use sequential hypothesis tests and require replication of failing results. When using EVT based extrapolation, monitor the model goodness of fit and sample sizes supporting the tail estimate. If goodness of fit degrades, mark the baseline stale and schedule a re-baseline campaign.

Case study: integrating RocqStat into embedded CI

Here is a condensed example of a real world integration pattern inspired by recent industry moves in 2026.

Context

An automotive module team runs a nightly hardware CI fleet. They used to store a single max runtime per test. After adopting statistical WCET analysis they implemented the following:

1. Instrumentation using DWT counters and light probes emitting CSV
2. Nightly campaign with 1000 runs across different seeds
3. RocqStat analysis to produce a 1e-6 violation probability WCET estimate
4. Baseline stored with versioning and linked to release artifacts
5. Gate in pipeline fails merges if new WCET > baseline + conservative guard band

Outcome

The team stopped shipping releases with hidden timing regressions. When regressions occurred they traced them quickly to a new library with a heavier cache footprint and reverted the change before deployment. The documented statistical baseline gave them evidence for the rollback decision during audits.

Tooling choices in 2026

RocqStat has become an industry reference for statistical WCET analysis and is being integrated into commercial toolchains. But it is not the only building block. Consider the following stack:

• Instrumentation: DWT, perf, ETM, LTTng
• Collection: custom measurement agents, archive storage, artifact registry
• Analysis: RocqStat, EVT libraries, in-house statistical scripts
• CI integration: GitLab/GitHub Actions with hardware runners or Jenkins with HIL farm
• Observability: Prometheus + Grafana, Alertmanager for alert rules

Checklist: get started in 6 weeks

Follow this prioritized plan to move from ad hoc timings to continuous WCET monitoring.

1. Identify 3 highest-risk code paths and add minimal cycle counter probes.
2. Stand up a dedicated hardware runner in CI and add a timing stage.
3. Run an initial baseline campaign and produce a RocqStat model.
4. Create a compare step that fails CI on clear regressions and tickets on soft alerts.
5. Publish dashboards showing percentile trends and add Alertmanager rules.
6. Automate artifact retention for raw samples and statistical models for audits.

Common pitfalls and how to avoid them

• Relying on VM timings: Do not trust VM-hosted timing. Use hardware runners for verification.
• No metadata: Without environment metadata baselines are not traceable. Capture everything.
• Single-run maxima: One-off max values are fragile and not defensible for certification.
• Overfitting EVT: Extrapolate only when the tail model fits well and you have sufficient samples.

Final recommendations

Timing must be treated as a continuous verification signal in safety pipelines. Use statistical WCET tools such as RocqStat to turn repeated measurements into auditable WCET estimates. Integrate those estimates into CI gates, use conservative guard bands, and instrument for reproducibility. Put observability and retention policies in place so every alert includes the data needed for fast, defensible triage.

Actionable takeaways

• Instrument critical paths with low-overhead cycle counters and emit machine readable samples.
• Run baseline campaigns with enough samples and use RocqStat or EVT to estimate WCET at the required violation probability.
• Integrate analysis into CI on hardware runners and gate merges when statistical tests indicate regressions.
• Use control charts for slow drift detection and require reproducible failing campaigns before blocking releases.
• Keep raw data, models, and environment metadata for audits and RCA.

Call to action

If your CI still treats timing as an afterthought start a targeted proof of concept this quarter. Instrument one module, run a baseline campaign, and add a RocqStat analysis stage to CI. If you want a practical checklist or example repo to get started, download our example CI templates and measurement adaptors or contact our team for a workshop on integrating statistical WCET into your verification pipeline.

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