Home Learning Paths ECU Lab Assessments Interview Preparation Arena Pricing Log In Sign Up

Lab: Complete CI/CD Testing Pipeline

PhaseActivityPass Criterion
Static analysisMAAB checks on modelZero failures
MiL testing10 test cases; collect coverage100% pass; branch >= 100%; MC/DC >= 90%
SiL buildCMake x86-64 build with coverageZero build errors
SiL testing15 SiL test cases; B2B vs MiL100% pass; max B2B diff < 1e-4
Coverage gateEnforce branch + MC/DC thresholdsAll thresholds met
Evidence packageAssemble ASPICE SWE.4 artefactsAll files present and named correctly

Exercise 1: Complete Pipeline Script

Bashrun_full_pipeline.sh
#!/bin/bash
# Complete MiL/SiL CI/CD pipeline
set -e
COMPONENT="SpeedController"
VERSION=$(git describe --tags --always)
REPORT_DIR="reports/${VERSION}"
mkdir -p "$REPORT_DIR"

echo "=== Stage 1: Static Analysis ==="
matlab -nodisplay -batch \
    "run_maab_check('${COMPONENT}'); exit"

echo "=== Stage 2: MiL Test ==="
matlab -nodisplay -batch \
    "run_mil_suite('${COMPONENT}','${REPORT_DIR}'); exit"

echo "=== Stage 3: SiL Build ==="
cmake -B build/ -DCMAKE_BUILD_TYPE=Release -DSIL_BUILD=1
cmake --build build/ --parallel $(nproc)

echo "=== Stage 4: SiL Test ==="
python3 -m pytest tests/sil/ -v \
    --junitxml="${REPORT_DIR}/sil_results.xml" \
    --html="${REPORT_DIR}/sil_report.html"

echo "=== Stage 5: Coverage Gate ==="
python3 check_coverage.py \
    --branch-min 100 \
    --mcdc-min 90 \
    "${REPORT_DIR}/coverage_summary.json"

echo "=== Stage 6: Evidence Package ==="
python3 assemble_evidence.py \
    --component "${COMPONENT}" \
    --version "${VERSION}" \
    --report-dir "${REPORT_DIR}"

echo ""
echo "PIPELINE COMPLETE: ${COMPONENT} v${VERSION}"
echo "Evidence: ${REPORT_DIR}/"

Exercise 2: Inject and Detect a Regression

Pythoninject_regression.py
"""Inject a regression into the model and verify CI catches it."""
# Step 1: Introduce a bug (change speed limit constant)
# In SpeedController.c: change SPEED_LIMIT from 120.0f to 119.0f

# Step 2: Run the pipeline -- expected: SiL test failure
# python3 -m pytest tests/sil/test_speed_limit_boundary.py -v
# Expected output:
# FAILED tests/sil/test_speed_limit_boundary.py::test_at_limit
# assert fault_active == False  (but got True at 119.9 km/h)

# Step 3: B2B comparison also catches it:
# MiL output at 119.5 km/h: fault=False
# SiL output at 119.5 km/h: fault=True  (regression detected)
# B2B max diff for FaultActive signal: 1.0 (>> 1e-4 tolerance)

# Step 4: Revert the bug; rerun; verify PASS
print("Regression injection test design:")
print("  1. Introduce bug (change constant)")
print("  2. Run pipeline: expect FAIL")
print("  3. Fix bug: revert change")
print("  4. Rerun pipeline: expect PASS")
print("  Purpose: verify CI pipeline reliably catches real regressions")

Summary

The complete CI/CD testing pipeline lab brings every technique from this course into a single automated workflow. The regression injection exercise is the most important validation: it proves that the pipeline actually catches real bugs rather than being a safety theatre exercise. Every automotive testing pipeline should have at least one deliberately-introduced regression test in its acceptance criteria -- if the pipeline does not catch a known bug, it cannot be trusted to catch unknown bugs. The evidence package output is what converts the technical testing work into ASPICE SWE.4 compliance: a structured folder of dated, version-labelled artefacts that can be presented to a safety assessor as the complete unit verification record for this software release.

🔬 Deep Dive — Core Concepts Expanded

This section builds on the foundational concepts covered above with additional technical depth, edge cases, and configuration nuances that separate competent engineers from experts. When working on production ECU projects, the details covered here are the ones most commonly responsible for integration delays and late-phase defects.

Key principles to reinforce:

  • Configuration over coding: In AUTOSAR and automotive middleware environments, correctness is largely determined by ARXML configuration, not application code. A correctly implemented algorithm can produce wrong results due to a single misconfigured parameter.
  • Traceability as a first-class concern: Every configuration decision should be traceable to a requirement, safety goal, or architecture decision. Undocumented configuration choices are a common source of regression defects when ECUs are updated.
  • Cross-module dependencies: In tightly integrated automotive software stacks, changing one module's configuration often requires corresponding updates in dependent modules. Always perform a dependency impact analysis before submitting configuration changes.

🏭 How This Topic Appears in Production Projects

  • Project integration phase: The concepts covered in this lesson are most commonly encountered during ECU integration testing — when multiple software components from different teams are combined for the first time. Issues that were invisible in unit tests frequently surface at this stage.
  • Supplier/OEM interface: This is a topic that frequently appears in technical discussions between Tier-1 ECU suppliers and OEM system integrators. Engineers who can speak fluently about these details earn credibility and are often brought into critical design review meetings.
  • Automotive tool ecosystem: Vector CANoe/CANalyzer, dSPACE tools, and ETAS INCA are the standard tools used to validate and measure the correct behaviour of the systems described in this lesson. Familiarity with these tools alongside the conceptual knowledge dramatically accelerates debugging in real projects.

⚠️ Common Mistakes and How to Avoid Them

  1. Assuming default configuration is correct: Automotive software tools ship with default configurations that are designed to compile and link, not to meet project-specific requirements. Every configuration parameter needs to be consciously set. 'It compiled' is not the same as 'it is correctly configured'.
  2. Skipping documentation of configuration rationale: In a 3-year ECU project with team turnover, undocumented configuration choices become tribal knowledge that disappears when engineers leave. Document why a parameter is set to a specific value, not just what it is set to.
  3. Testing only the happy path: Automotive ECUs must behave correctly under fault conditions, voltage variations, and communication errors. Always test the error handling paths as rigorously as the nominal operation. Many production escapes originate in untested error branches.
  4. Version mismatches between teams: In a multi-team project, the BSW team, SWC team, and system integration team may use different versions of the same ARXML file. Version management of all ARXML files in a shared repository is mandatory, not optional.

📊 Industry Note

Engineers who master both the theoretical concepts and the practical toolchain skills covered in this course are among the most sought-after professionals in the automotive software industry. The combination of AUTOSAR standards knowledge, safety engineering understanding, and hands-on configuration experience commands premium salaries at OEMs and Tier-1 suppliers globally.

← PreviousRegression Testing Strategies