Timing Analysis in CI: Integrating WCET Tools Like RocqStat into Automotive Pipelines

Why timing regressions still break automotive CI — and how you stop them earlier

Every build that lands late in an automotive CI pipeline risks a timing regression that can invalidate weeks of integration work, force expensive HIL reruns, and create certification headaches under ISO 26262 and SOTIF. Teams tell us the same pain: timing and WCET (worst-case execution time) analysis are too late, too slow, and too disconnected from regular developer workflows. Vector’s January 2026 acquisition of RocqStat changes that. By embedding RocqStat’s static timing analysis into VectorCAST and CI toolchains, engineering teams can shift rigorous timing checks left — catching timing regressions in pre-merge checks and short CI cycles rather than during final integration.

The 2026 context: why timing matters more than ever

Through late 2025 and into 2026, automotive software became even more central to vehicle capabilities: over-the-air updates, domain controllers, and ADAS stacks increased the complexity and the number of timing-dependent interactions. Regulators and OEMs have tightened expectations for demonstrable timing safety in software-defined vehicles, and suppliers must provide stronger evidence of timing behavior across hardware variants. At the same time, CI/CD practices matured in embedded teams: containers, reproducible toolchains, and aggressive gatekeeping are now mainstream. The missing link was automated, reliable timing analysis that fits CI constraints — fast enough for frequent runs and precise enough for safety cases. Vector + RocqStat aims directly at that gap.

What the Vector–RocqStat integration promises

• Unified workflows: WCET estimation and functional verification in the same toolchain (VectorCAST), reducing context switching for engineers.
• Earlier feedback: static timing checks integrated into CI pipelines (pre-merge and CI gating) so regressions are detected within minutes-to-hours, not days.
• Continuity and expertise: existing RocqStat analysis know-how brought into Vector’s teams to accelerate feature delivery and maintain depth in timing research and tooling.
• Traceability for certification: combined artifacts that link unit/integration tests with timing evidence, simplifying ISO 26262 deliverables and audit trails.

“Timing safety is becoming a critical...” — Vector (announcement, Jan 2026)

How timing analysis fits into modern CI: strategy and stages

For effective CI integration, treat timing analysis as a set of complementary checks, not a single monolith. Each check has different costs and value:

• Fast static WCET on changed functions — lightweight, change-focused, suitable for pre-merge checks.
• Incremental whole-module WCET — runs on merge to main; balances depth and runtime using cached results and dependency analysis.
• Full-system WCET — deep, expensive analyses (e.g., inter-core interference) reserved for nightly or release pipelines and for certification artifacts.
• Measurement-based verification (SIL/HIL) — empirical validation runs once static analysis is stable or as part of release acceptability tests.

Design principles for CI timing checks

1. Make timing first-class in the pipeline — allocate dedicated stages for timing so results appear in the same CI report as functional test coverage and static analysis findings.
2. Use change-based analysis — analyze only changed translation units and their call-impact to keep feedback fast.
3. Enforce baselines and budgets — set per-module and per-task timing budgets and fail merges when budgets are exceeded.
4. Automate evidence packaging — collect WCET artifacts, config, tool versions, and logs into a reproducible bundle for audits.
5. Support heterogeneous hardware — parameterize analyses for different target caches, clocks, and RTOS configurations used across variants.

Practical CI integration patterns with RocqStat + VectorCAST

Below are concrete integration patterns for common CI systems. These examples assume Vector will expose RocqStat functionality through VectorCAST APIs or a CLI (Vector’s announced roadmap indicates a unified environment; adapt the examples to the final API names your team receives).

1) Pre-merge timing checks (fast WCET on changed code)

Goal: provide quick feedback to developers before a merge — targeted static WCET for changed functions and their callers. Key tactics:

• Run a lightweight RocqStat analysis in a container image that contains the cross-toolchain and consistent compiler flags.
• Restrict analysis scope to changed C/C++ files and immediate call graph up to a configurable depth.
• Compare computed WCETs against per-function budgets stored in the repo or a central policy service.

<!-- GitLab CI job (conceptual) -->
stages:
  - build
  - test
  - timing

timing_fast:
  stage: timing
  image: registry.example.com/vector-rocqstat:2026.01
  script:
    - export TOOLCHAIN=/opt/gcc-arm-none-eabi-10
    - ./vectorcast build --profile ci
    - ./rocqstat analyze --changed-files $(git diff --name-only $CI_MERGE_REQUEST_TARGET_BRANCH) --budget-file timing_budgets.json --output wcet_fast.json
    - ./rocqstat report --format json --output wcet_report.json
  artifacts:
    paths:
      - wcet_fast.json
      - wcet_report.json
  rules:
    - if: $CI_PIPELINE_SOURCE == "merge_request_event"

2) Merge-to-main incremental analysis

Goal: when a merge succeeds, run a deeper analysis that updates cached WCET results and creates a reproducible artifact for traceability.

• Use VectorCAST to run unit/integration tests then kick off RocqStat with full module context.
• Leverage artifact caching: reuse previous analysis results for unchanged object files to avoid redoing expensive analyses.
• Store a signed artifact (tool + config + results) in an artifact repository for certification evidence.

3) Nightly full-system timing and interference checks

Goal: deep WCET analysis across tasks, inter-core interference, and RTOS interactions. These runs are expensive and belong in nightly pipelines or release validation.

• Parameterize hardware platform models — cache sizes, bus arbitration policies, and clock trees.
• Combine static RocqStat results with measurement traces from SIL/HIL to calibrate models.
• Produce a consolidated timing report mapped to AUTOSAR tasks or functional safety requirements for sign-off.

Dealing with multi-core and interference: realistic expectations

Multi-core timing is one of the hardest problems for WCET tools. Static WCET tools (like RocqStat) give conservative bounds for single-core or for well-abstracted shared resources. For multi-core platforms, a hybrid approach is usually necessary:

• Isolate critical tasks using partitioning, time partitioning, or hardware isolation so static WCET assumptions hold per core.
• Model shared resources (buses, caches) in the static analysis where possible; otherwise, apply measured interference budgets derived from microbenchmarks and SIL runs.
• Use compositional analysis — compute WCETs for partitioned components and then compose them with interference margins to get system-level guarantees.

Vector’s plan to combine RocqStat with VectorCAST gives teams a practical path: use fast static checks early, and schedule comprehensive multi-core interference analyses as targeted nightly or release tasks where a disciplined partitioning strategy is enforced.

Data flows and artifact management: what to collect in CI

For traceability and reproducibility, a CI run that includes timing analysis should emit a standard artifact set. Make this part of the pipeline policy:

• Toolchain manifest — compiler versions, linker flags, and build system invocation.
• Analysis configuration — RocqStat/VectorCAST config files, hardware model parameters, and budget files.
• Results — WCET reports, hot-path listings, and confidence metrics.
• Inputs — object files or checksums used for the analysis to ensure reproducibility.
• Signed evidence bundle — store artifacts in an immutable repository with cryptographic signatures for later audit.

Actionable checklist: add WCET checks to your CI in 8 steps

1. Inventory timing-critical modules — map code units to functional safety requirements and per-task deadlines.
2. Establish per-module timing budgets — set conservative budgets initially, refine based on data.
3. Containerize the analysis — create a reproducible container with VectorCAST + RocqStat and the cross-toolchain.
4. Set up a fast pre-merge job — run change-based RocqStat checks; fail the merge on budget violations.
5. Configure merge-to-main incremental runs — update cached WCET baselines and generate signed artifacts.
6. Schedule nightly deep analyses — multi-core and interference analyses with broader models.
7. Integrate with ticketing/alerts — push timing regressions to the issue tracker and to Slack/Teams with full context.
8. Automate evidence packaging — bundle and sign artifacts for ISO 26262 audits and supplier handoffs.

Example: a small workflow that stopped a timing regression within hours

Hypothetical but typical: a Tier‑1 supplier integrated RocqStat into their GitLab CI pre-merge stage. A developer changed a scheduling function that increased worst-case path length in a safety task. The fast RocqStat pre-merge job analyzed only the modified translation units and callers, reported a 23% increase in the WCET for the task, and blocked the merge. The developer adjusted the loop-bound logic and re-ran. Because the analysis ran in the pre-merge window, the issue was resolved in hours instead of surfacing after the integration phase and requiring a full HIL cycle.

Integration pitfalls and how to avoid them

Pitfall: noisy false positives

Static timing analyses can be conservative. Reduce noise by:

• Using realistic hardware models (cache, pipelines).
• Applying measurement-based calibration for common library code.
• Tuning call-depth and scoping in pre-merge runs to prioritize actionable findings.

Pitfall: slow CI jobs

Mitigate with change-based analysis, cache reuse, and tiered pipelines (fast pre-merge vs nightly deep runs). Containerized toolchains and dedicated runners can keep analysis latency predictable.

Pitfall: license and concurrency limits

Commercial timing tools are typically licensed. Plan license pools for CI runners, or use floating license servers and queueing policies so that pre-merge checks remain fast and reliable.

Measuring success: KPIs to track

• Mean time to detection (MTTD) for timing regressions — target a reduction from days to hours.
• Number of timing-related late integration failures per release — aim for continuous decline after integration.
• Pre-merge pass rate — percentage of merge requests that fail timing checks before mainline integration (goal: actionable failure rate, not noise).
• Artifact completeness — percent of releases with signed timing-evidence bundles for certification.

Toward the future: predictions for timing analysis in CI by 2028

Based on trends in 2025–2026, expect these developments through 2028:

• Toolchain consolidation — vendors will continue integrating timing, testing, and traceability into single shelf tools (Vector’s move is an early example).
• Better compositional analyses — increasing research and product focus on compositional WCET that scales to complex multi-core ECUs.
• Standardized evidence formats — CI artifact formats for timing evidence (signed, reproducible bundles) will become industry best practice, making supplier handoffs smoother.
• CIA/DevSecOps convergence — timing analysis will be a first-class citizen in compliance and DevOps pipelines, with automated traceability into safety cases.

Final recommendations — what to do next (practical, immediate)

1. Run a 2-week spike: create a VectorCAST + RocqStat container, instrument a small timing-critical module, and add a pre-merge timing job. Measure runtime and false-positive rate.
2. Define realistic per-module budgets with stakeholders (SWE, system architects, safety engineers).
3. Implement a proof-of-concept incremental analysis on a feature branch and integrate results into PR comments and CI dashboards.
4. Plan a nightly full-system timing run and produce an evidence bundle for one release to test the certification workflow.

Conclusion — catching timing issues earlier is now practical

Vector’s acquisition of RocqStat gives teams a pragmatic path to embed rigorous timing analysis and WCET estimation earlier in CI. The combined toolchain addresses a pressing need in automotive embedded development: shift-left timing checks that are fast enough for developer workflows, deep enough for certification, and traceable enough for audits. The practical approach is hybrid: lightweight, change-focused static checks in pre-merge checks stages; incremental whole-module analysis on merge; and full-system interference analysis on nightly or release pipelines. Implementing this pattern reduces late surprises, shortens feedback loops, and strengthens safety cases — all critical for software-defined vehicles in 2026 and beyond.

Call to action

Ready to integrate RocqStat-based timing checks into your CI pipeline? Wecloud.pro helps automotive teams design reproducible CI workflows, containerized toolchains, and certification-ready artifact flows using VectorCAST and timing analysis best practices. Contact us for a 2-week pipeline spike workshop and a tailored roadmap to move WCET checks left in your development cycle.

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