Machine learning enhances fuzzing for better software testing.
problem Challenges in traditional fuzzing.
method Applications of machine learning to improve fuzzing.
result Machine learning tools have successfully addressed fuzzing bottlenecks.
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4 results for “fuzzing”
A new method for testing deep learning models using CGF.
problem Traditional testing methods fail to cover corner cases in DNNs.
method Monte Carlo Tree Search for coverage-guided search.
result Generated inputs result in higher coverage than previous methods.
Machine learning models are notoriously difficult to interpret and debug. This is particularly true of neural networks. In this work, we introduce automated software testing techniques for neural networks that are well-suited to discovering errors which occur only for rare inputs. Specifically, we develop coverage-guid…
DeepEvolution improves testing of deep neural networks by generating diverse test cases.
problem Lack of diversity in generated test cases from random fuzzing or transformations.
method Search-based approach using metaheuristics to ensure diversity in test cases.
result DeepEvolution significantly increases neuronal coverage and detects latent defects.