stress testing

**Stress Testing** for ML models is the **systematic evaluation of model performance under extreme or challenging conditions** — pushing inputs beyond typical operating ranges to identify failure modes, performance degradation, and the limits of reliable model operation. **Stress Testing Approaches** - **Distribution Shift**: Test on data from different distributions (different fab, different product, different time period). - **Extreme Values**: Feed inputs at the boundaries or beyond the training data range. - **Noise Injection**: Add increasing levels of noise to inputs to find the noise threshold for failure. - **Adversarial**: Apply adversarial perturbations of increasing strength. **Why It Matters** - **Failure Discovery**: Stress testing reveals failure modes invisible in standard accuracy evaluation. - **Operating Envelope**: Defines the reliable operating envelope of the model — where it can and cannot be trusted. - **Production Safety**: Models deployed in semiconductor fabs must be tested under stress before controlling real processes. **Stress Testing** is **pushing the model to its limits** — finding where and how the model breaks to ensure safe deployment.