Siegfried Steckenbiller

Evaluating DL Test Adequacy Metrics: A Correlation Study with DeepCrime’s Mutation Score

Evaluating the quality of test suites for Deep Learning (DL) systems generally relies either on structural coverage criteria (e.g., Neuron Coverage), distribution-aware metrics (e.g., Surprise Adequacy), or mutation testing. While mutation testing offers the most reliable measure of fault-detection, it is often prohibi...