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Found 6 papers, 2 papers with code
DeepSample: DNN sampling-based testing for operational accuracy assessment
Companies incur in high costs for testing DNN with datasets representative of the inputs expected in operation, as these need to be manually labelled.
Reinforcement Learning for Online Testing of Autonomous Driving Systems: a Replication and Extension Study
Our extension aims at eliminating some of the possible reasons for the poor performance of RL observed in our replication: (1) the presence of reward components providing contrasting or useless feedback to the RL agent; (2) the usage of an RL algorithm (Q-learning) which requires discretization of an intrinsically continuous state space.
Reasoning-Based Software Testing
We introduce Reasoning-Based Software Testing (RBST), a new way of thinking at the testing problem as a causal reasoning task.
Iterative Assessment and Improvement of DNN Operational Accuracy
We propose DAIC (DNN Assessment and Improvement Cycle), an approach which combines ''low-cost'' online pseudo-oracles and ''high-cost'' offline sampling techniques to estimate and improve the operational accuracy of a DNN in the iterations of its life cycle.
Automated hypothesis generation via Evolutionary Abduction
Given an effect, the abductive reasoning allows advancing a plausible set of explanatory hypotheses for its causes.
Operation is the hardest teacher: estimating DNN accuracy looking for mispredictions
Deep Neural Networks (DNN) are typically tested for accuracy relying on a set of unlabelled real world data (operational dataset), from which a subset is selected, manually labelled and used as test suite.
Software Engineering
