| Deliverable | Target | Evidence |
|---|---|---|
| Test suite | 10+ test cases; all SSRs covered | Simulink Test .mldatx file |
| Coverage | 100% branch; 100% state+transition; 90%+ MC/DC | Coverage HTML report |
| Traceability | Every test case linked to SSR | Traceability matrix |
| ASPICE report | Test spec + results + coverage consolidated | PDF report |
Lab: Complete MiL Test Suite
Exercise 1: Automated MiL Test Runner
MATLABrun_mil_suite.m
% Complete MiL test suite execution with coverage and reporting
model = "SpeedController";
test_file = "tests/SpeedController_Tests.mldatx";
% Step 1: Rebuild harnesses (in case model changed)
sltest.harness.rebuild("SpeedController/PID_Controller",
"PID_TestHarness");
% Step 2: Run with coverage
covSettings = cvsettings(model);
set(covSettings, "RecordCoverage", true);
set(covSettings, "CovMetricSettings", "dcmt");
runOpts = sltest.testmanager.TestRunnerOptions;
runOpts.CoverageSettings = covSettings;
ts = sltest.testmanager.loadTests(test_file);
results = sltest.testmanager.run(ts, "Options", runOpts);
% Step 3: Check pass rate
pass_rate = results.NumPassed / results.NumTests * 100;
fprintf("Pass rate: %.1f%% (%d/%d)\n",
pass_rate, results.NumPassed, results.NumTests);
assert(results.NumFailed == 0, "MiL test failures detected");
% Step 4: Check coverage thresholds
cov = results.getCoverageData.coverageData;
assert(cov.decision.percentage >= 100, "Branch coverage < 100%%");
assert(cov.mcdc.percentage >= 90, "MC/DC coverage < 90%%");
% Step 5: Generate consolidated report
sltest.testmanager.report(results, "MiL_Test_Report.pdf",
"IncludeCoverage", true,
"IncludeRequirements", true,
"IncludeSimulationSignalPlots", true);Summary
The complete MiL test suite lab produces the ASPICE SWE.4 evidence package in a single automated script execution. The assertion-based quality gates (pass rate 100%, branch coverage 100%, MC/DC > 90%) turn the script into a quality gate: if any threshold is not met, the script exits with an error and the CI pipeline fails. This is the correct way to integrate MiL testing into continuous integration -- not as an optional report generation step, but as a hard quality gate that blocks progression to code generation and SiL testing until the model meets its verification targets.
🔬 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
- 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'.
- 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.
- 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.
- 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.