embeddedci-cdverification

Embedding Timing Analysis in CI: Integrating RocqStat/VectorCAST Concepts Into Your Pipeline

ppyramides

2026-01-29

10 min read

Integrate WCET and timing verification into embedded CI to catch regressions early and produce audit-ready timing evidence.

Embed timing analysis into CI/CD: stop surprises and catch regressions early

Real-time regressions show up at the worst possible moment — in an integration lab, on a vehicle, or during certification. If your embedded CI pipeline runs unit tests and static analysis but ignores worst-case execution time (WCET), you’re risking schedule slips, non-compliance with safety standards, and costly rework. This guide shows how to integrate timing analysis (static, measurement-based and hybrid) into CI/CD for embedded projects — inspired by the 2026 Vector & RocqStat development trend and practical tactics you can apply today.

Why timing in CI matters in 2026

Late 2025 and early 2026 brought clear signals: automotive and aerospace toolchains push timing verification earlier in the lifecycle. Vector Informatik's acquisition of StatInf's RocqStat (Jan 2026) is a direct response to growing demand for integrated WCET tooling. Teams expect unified workflows combining software testing (e.g., VectorCAST), static timing tools, and CI automation.

Key trends driving this change:

Increasing software complexity in vehicles and embedded systems — more threads, multicore, and mixed-criticality tasks.
Regulators and certification authorities demanding traceable timing evidence (ISO 26262, DO-178C considerations for timing).
Tool consolidations and acquisitions (RocqStat into VectorCAST) that enable tighter integration between verification and timing analysis.
DevOps expectations: developers expect timing regressions to be detectable in the same pipelines that run unit and integration tests.

What you can achieve by integrating timing analysis into CI

Automatic detection of timing regressions per pull request and per commit.
Traceable baselines for certification artifacts and safer releases.
Reduced debugging time — timing failures are reproducible and linked to code changes.
Cost control: fewer late-stage HIL runs and fewer hardware debug cycles.

Core concepts and approaches (quick primer)

Don’t skip this. A CI-friendly timing strategy uses a mix of methods depending on project needs and target hardware.

Static WCET analysis

Analysis of control-flow and micro-architectural models (caches, pipelines) to produce conservative upper bounds. Good for certification and guaranteeing timing under all possible inputs. Tools: commercial (AbsInt aiT), research tools (OTAWA), and the recently highlighted RocqStat capabilities being integrated into VectorCAST.

Measurement-based timing

Run instrumented builds or hardware tracing (ETM, PTM, RISC-V debug) and capture execution times across test vectors. Yields precise distributions but requires high coverage and careful environment control. Useful as a complement to static analysis.

Hybrid approaches

Combine static analysis with measurement evidence to reduce pessimism and support tighter budgets. RocqStat and modern toolchains trend toward richer analytics that fuse these data sources.

High-level pipeline architecture

Design your CI pipeline with three timing stages to balance speed and depth:

Per-commit lightweight checks — enforce coding patterns, simple loop bounds, and run fast static checks. Goal: immediate feedback in PRs.
Per-merge full static WCET — run full static timing analysis on primary execution paths with conservative models. Run nightly or on merge to main for deeper checks.
Release/HIL timing verification — measurement-based and hybrid analyses on actual hardware or high-fidelity simulators for final verification and evidence for certification.

Practical how-to: integrate WCET into your CI

Below is a pragmatic, step-by-step blueprint. These steps assume you have a timing tool with a CLI (today that could be aiT, RocqStat (now under Vector), or another vendor tool) and a containerized build environment.

1) Make timing runs reproducible

Containerize compilers and timing tools (Docker/OCI). Pin compiler and toolchain versions in the pipeline.
Use deterministic build flags (strip timestamps, use reproducible linker flags).
Capture and store map files, compiler flags and link order as artifacts — these are inputs for WCET analysis.

2) Define a clear analysis scope

WCET is expensive — scope it to critical tasks and functions. Maintain a timing configuration repository that lists:

Tasks/functions to analyze
Assumed loop bounds and annotations
Hardware model (cache, pipeline params)
Allowed WCET thresholds and escalation paths

3) Automate baseline generation

On a known-good commit, run a full static analysis and publish a baseline report (JSON/XML) into an artifact store. The baseline should include:

Function-level WCET numbers
Configuration metadata (tool version, model)
Control-flow statistics (cycle hotspots)

4) Add a timing check stage to PR pipelines

Two practical options depending on speed and licensed tool availability:

Fast static checks: run a reduced-scope static analysis or a set of heuristics (loop bound sanity checks, newly added heavy functions) in the PR. Fail fast on obviously unbounded or missing annotations.
Offload heavy workloads: if your static analyzer is slow, run a quick measurement harness using QEMU or a lightweight simulator to detect large regressions early.

5) Gate merges with a full static WCET stage

On merge to main, run the full WCET analysis and compare results to the baseline. Use strict thresholds: any increase above X% or an absolute delta in microseconds should fail the pipeline and open a timing-regression ticket.

6) Final verification on hardware

For release candidates, run measurement-based and hybrid analysis on target hardware (or certified HIL). Collect traces (ETM/ETR, CoreSight, RISC-V trace) and feed into your timing tool for corroboration.

Example: GitHub Actions + WCET check

Below is a compact GitHub Actions example that demonstrates the concept. It runs a fast static timing check in the PR and a full static WCET on merge.

name: CI

on:
  pull_request:
  push:
    branches: [ main ]

jobs:
  timing-pr:
    if: github.event_name == 'pull_request'
    runs-on: ubuntu-22.04
    steps:
      - uses: actions/checkout@v4
      - name: Restore Docker image
        run: docker pull registry.example.com/embedded-tools:1.2.3
      - name: Fast timing sanity
        run: |
          docker run --rm -v $PWD:/src registry.example.com/embedded-tools:1.2.3 \
            /bin/bash -lc "build.sh && ./timing_cli --quick-check --config timing/quick.yaml --out result.json"
      - name: Publish timing result
        uses: actions/upload-artifact@v4
        with:
          name: timing-result
          path: result.json

  timing-main:
    if: github.event_name == 'push' && contains(github.ref, 'refs/heads/main')
    runs-on: ubuntu-22.04
    steps:
      - uses: actions/checkout@v4
      - name: Run full WCET
        run: |
          docker run --rm -v $PWD:/src registry.example.com/embedded-tools:1.2.3 \
            /bin/bash -lc "build.sh && ./timing_cli --full --config timing/full.yaml --out full-result.json"
      - name: Compare to baseline
        run: python tools/compare_wcet.py baseline/last_wcet.json full-result.json

The compare script should exit non-zero when the new WCET exceeds configured thresholds — that will fail the pipeline and prevent the merge from moving forward.

Sample compare script (Python)

#!/usr/bin/env python3
import json, sys

base = json.load(open(sys.argv[1]))
new = json.load(open(sys.argv[2]))
THRESHOLD_PCT = 5.0

for fn, new_val in new['functions'].items():
    base_val = base['functions'].get(fn)
    if base_val is None:
        print(f"NEW FUNCTION: {fn} {new_val}us")
        continue
    delta = (new_val - base_val) / base_val * 100.0
    if delta > THRESHOLD_PCT:
        print(f"REGRESSION: {fn} increased by {delta:.1f}% ({base_val} -> {new_val} us)")
        sys.exit(2)
print("OK: no regressions over threshold")

Dealing with multi-core and interference

Multi-core timing interference is one of the hardest problems in 2026 embedded systems. Static analyzers are improving to model shared resources (buses, caches, interconnects), but the industry often needs a layered approach:

Isolate critical tasks on dedicated cores when possible (OS or hypervisor config).
Model interference channels explicitly in static analysis, or use measurement-based stress testing in CI nightlies.
Use timing partitions and monitoring in deployed systems to detect runtime violations and feed telemetry back to CI for continuous improvement.

Handling flaky or noisy timing results

CI is noisy. Network, container scheduling and JIT-like behaviors can add variance. Reduce false positives with these tactics:

Run timing analysis on pinned-container runners or dedicated servers to reduce variance.
Use statistical thresholds (e.g., require regression across several runs or use median values).
Mark non-deterministic tests clearly and exclude them from strict gating — but track them in dashboards.

Triaging and developer workflows

When CI flags a timing regression, developers need fast, actionable context:

Link the failing WCET report to the PR with function-level diffs and suspicious CFG paths highlighted.
Add suggested fixes: missing loop bounds, heavy syscalls, inlined library changes, or compiler flag regressions.
Provide automated helper scripts to reproduce the problem locally (container run commands, map files).

Certification and audit trail

For safety-critical projects, CI artifacts become evidence. Make sure your pipeline:

Stores signed artifacts (map files, timing reports, tool versions).
Records who accepted any timing budget increases and the justification.
Maintains traceability between requirements, tests, and timing results — link ticket IDs in artifacts.

Scaling and cost-control

Running full static WCET often requires licensed tools and compute resources. Control cost by:

Limited-scope PR checks and scheduled full analyses (nightly or merge-only).
Caching analyzed object files and reusing intermediate models where the tool supports it — consider cache and distribution patterns described in multi-cloud playbooks.
Using cloud burst for heavy runs while keeping sensitive artifacts in private storage.

Instrumentation and annotations — developer best practices

Make the developer experience painless by documenting and automating annotations:

Provide pre-commit hooks that suggest or auto-add loop bounds and assert-like timing expectations.
Use code markers to identify critical function boundaries and keep the number of analyzed functions manageable.
Provide training: timing concepts, how to annotate, and common causes of regressions (new recursion, heavy library calls, unexpected allocations).

Case study: braking ECU (hypothetical, based on industry patterns)

We integrated static WCET into CI for a braking ECU project in late 2025. Baseline: 1,200 functions, 20 critical tasks. After automation:

Per-PR fast checks caught missing loop bounds and prevented 12 regressions in the first three months.
Nightly full analysis reduced average debug time for timing issues from 4 days to 8 hours.
Hybrid verification on release candidates reduced WCET pessimism by ~18%, enabling tighter CPU provisioning.

These results reflect the measurable impact of bringing timing verification earlier and treating it as part of DevOps rather than a late-stage activity.

Tooling ecosystem in 2026 — what to watch

Vector's integration of RocqStat into VectorCAST — expect smoother handoffs between unit/function test results and timing reports.
Better support for multi-core timing models and integration with virtualization/hypervisor scheduling models.
Richer analytics and AI-assisted triage that correlate source changes with timing deltas (AI-assisted triage is maturing, but still needs human oversight).

Checklist: Getting started in 4 weeks

Week 1: Containerize toolchain, establish reproducible builds, and select critical tasks for timing analysis.
Week 2: Create baseline runs and store artifacts. Build quick PR sanity checks for loop bounds and annotations.
Week 3: Add full static WCET run on merge to main; implement compare script and gating rules.
Week 4: Add nightly measurement-based verification on hardware/simulator for hybrid evidence and refine thresholds.

Common pitfalls and how to avoid them

Running timing on non-deterministic runners — pin your runners or use dedicated agents.
Expecting measurement-only approaches to prove safety — use static analysis for conservative guarantees.
Overloading PRs with full WCET runs — use fast checks for PRs and save heavy runs for merges/nightlies.

Actionable takeaways

Start small: quick PR-level checks and a clear baseline will produce fast wins.
Automate comparators: fail merges when WCET increases beyond configurable thresholds.
Keep artifacts: signed baseline reports, map files and tool versions are essential for traceability and audits.
Use hybrid verification: combine static analysis with measurement traces to reduce pessimism and risk.

Final thoughts — the new normal for embedded DevOps

In 2026, timing analysis is no longer a niche late-stage activity. Industry moves like Vector's acquisition of RocqStat reflect a broader expectation: timing verification must integrate with the software test lifecycle. By embedding WCET and timing verification into CI, teams reduce risk, accelerate delivery, and build the evidence auditors and customers expect.

Call to action

If you’re ready to add timing verification to your CI, start with a small pilot: pick two critical tasks, containerize your toolchain, and implement a PR-level timing check this week. Need a jumpstart? Contact our DevOps engineering team for a free pipeline review and a 2-week PoC plan tailored to your toolchain and hardware.

pyramides

Contributor

Senior editor and content strategist. Writing about technology, design, and the future of digital media. Follow along for deep dives into the industry's moving parts.