Test Automation
AI-Enabled Regression Test Optimization
Intelligently reducing regression-test execution effort while preserving defined coverage and confidence.
Introduction
- ✓Hearing-aid firmware applies more gain to quiet sound than to loud, compressing everyday sound into the range a wearer can still use. A change anywhere in that firmware can degrade the behaviour in ways inaudible on a bench but obvious to a wearer.
- ✓A development board runs the firmware; a host streams audio to it over Bluetooth Low Energy, captures what comes back, and measures the result. The board serves one connection at a time and each test occupies it exclusively, so running the whole suite on every build is expensive.
- ✓The framework reads the state of the board, scores how likely the change under test is to have broken something, and generates only the tests the evidence supports — raising the intensity and re-running on any failure, recovering the coverage the first pass deliberately omitted.
Engineering Demonstrated
- ✓Risk-based test selection (weighted threshold model)
- ✓AI planning layer with schema-constrained LLM and offline fallback
- ✓Dynamic pytest generation (AST-validated)
- ✓Failure-driven escalation and re-run under stress
- ✓BLE / GATT transport to nRF52840 / nRF5340 (Zephyr, Cortex-M4F)
- ✓On-device WDRC audio DSP verification (SNR loopback)
- ✓JUnit XML / CI-compatible reporting
Where This Can Help
- ✓Embedded firmware regression testing
- ✓Hearing-aid and audio DSP validation
- ✓Reducing regression execution time and effort
- ✓BLE device test automation
- ✓Risk-prioritised test suites in CI pipelines
- ✓Coverage-preserving test reduction
Open Source
Explore the source code and documentation for the AI-Enabled Regression Test Optimization project.
View Source Code