DeCaF combines counterfactual generators and causal models to identify minimal input signal changes that fix CPS failures and derives interpretable assertions that generalize the recovery conditions.
Simulation-based test case generation for unmanned aerial vehicles in the neighborhood of real flights,
2 Pith papers cite this work. Polarity classification is still indexing.
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Test validators generated via genetic programming using the Ochiai SBFL formula are more accurate and robust to flakiness than alternatives from Tarantula, Naish, decision trees, or rules, with 88.7% alignment to known requirements in CPS case studies.
citing papers explorer
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Towards Counterfactual Explanation and Assertion Inference for CPS Debugging
DeCaF combines counterfactual generators and causal models to identify minimal input signal changes that fix CPS failures and derives interpretable assertions that generalize the recovery conditions.
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Automated Test Validators for Flaky Cyber-Physical System Simulators: Approach and Evaluation
Test validators generated via genetic programming using the Ochiai SBFL formula are more accurate and robust to flakiness than alternatives from Tarantula, Naish, decision trees, or rules, with 88.7% alignment to known requirements in CPS case studies.