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Found 7 papers, 3 papers with code
Perfectly Parallel Fairness Certification of Neural Networks
Recently, there is growing concern that machine-learning models, which currently assist or even automate decision making, reproduce, and in the worst case reinforce, bias of the training data.
DeepMutation: Mutation Testing of Deep Learning Systems
To do this, by sharing the same spirit of mutation testing in traditional software, we first define a set of source-level mutation operators to inject faults to the source of DL (i. e., training data and training programs).
Software Engineering
DeepSearch: A Simple and Effective Blackbox Attack for Deep Neural Networks
Although deep neural networks have been very successful in image-classification tasks, they are prone to adversarial attacks.
DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems
Deep learning (DL) defines a new data-driven programming paradigm that constructs the internal system logic of a crafted neuron network through a set of training data.
Combinatorial Testing for Deep Learning Systems
Deep learning (DL) has achieved remarkable progress over the past decade and been widely applied to many safety-critical applications.
High-Assurance Separation Kernels: A Survey on Formal Methods
In accordance with the analytical framework, a comprehensive analysis and discussion of related work are presented.
Software Engineering
Detecting Critical Bugs in SMT Solvers Using Blackbox Mutational Fuzzing
Formal methods use SMT solvers extensively for deciding formula satisfiability, for instance, in software verification, systematic test generation, and program synthesis.
Software Engineering
