RiskBlend: A Multi-Signal Framework for Test Input Prioritization in Machine Learning Regression Testing
RiskBlend is a classifier-agnostic framework for prioritizing test inputs during ML regression testing, combining four risk signals: historical failure patterns, prediction shift, decision-boundary shift, and neighborhood change The framework uses validation-learned APFD-squared weighting to optimally combine these complementary signals Evaluated across 1,200 experimental configurations (4 datasets, 5 classifiers, 4 regression-update scenarios, 15 random seeds), RiskBlend achieved the highest av
Analysis
TL;DR
- RiskBlend is a classifier-agnostic framework for prioritizing test inputs during ML regression testing, combining four risk signals: historical failure patterns, prediction shift, decision-boundary shift, and neighborhood change
- The framework uses validation-learned APFD-squared weighting to optimally combine these complementary signals
- Evaluated across 1,200 experimental configurations (4 datasets, 5 classifiers, 4 regression-update scenarios, 15 random seeds), RiskBlend achieved the highest average APFD in all 80 dataset-classifier-scenario combinations
- Improvements of up to 0.32 APFD over the strongest baseline were observed
- Confidence-based methods remain competitive primarily for linear classifiers on sparse categorical features, attributed to feature-space geometry
Why It Matters
ML model retraining introduces regression faults where previously correct predictions become incorrect, and detecting these faults is expensive due to reliance on human annotation or simulation. This work provides a practical, classifier-agnostic solution for prioritizing which test inputs to verify first under limited budgets, directly addressing a critical pain point in MLOps and continuous integration pipelines for machine learning systems.
Technical Details
- Four risk signals: (1) Historical failure patterns from previous regression cycles, (2) Prediction shift measuring changes in predicted labels between model versions, (3) Decision-boundary shift quantifying how classification boundaries move, and (4) Neighborhood change capturing how local input distributions are affected by model updates
- APFD-squared weighting: Signal combination is learned via validation on held-out data, optimizing for Average Percentage of Faults Detected squared (APFD²), a metric that penalizes late fault detection more heavily
- Classifier-agnostic design: The framework is model-agnostic and was evaluated across five different classifier types, demonstrating broad applicability
- Extensive empirical validation: 1,200 configurations spanning four datasets, five classifiers, four regression-update scenarios, and 15 random seeds provide robust statistical grounding
- Key finding: Cross-version behavioral signals provide complementary information that single-model confidence scores cannot capture, with the largest gains observed in non-linear classifiers and dense feature spaces
Industry Insight
- Organizations running continuous ML pipelines should adopt multi-signal prioritization rather than relying solely on confidence scores, as the latter fail to capture cross-version behavioral changes that are the primary source of regression faults
- The classifier-agnostic nature of RiskBlend means it can be integrated into existing MLOps toolchains without requiring model-specific adaptations, reducing implementation friction
- For teams with linear classifiers and sparse categorical features, simpler confidence-based approaches may still be sufficient, allowing resource allocation to more complex scenarios where RiskBlend's gains are largest
Disclaimer: The above content is generated by AI and is for reference only.