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Found 3 papers, 2 papers with code
Bug Characterization in Machine Learning-based Systems
Based on our results, fixing ML bugs are more costly and ML components are more error-prone, compared to non-ML bugs and non-ML components respectively.
Bugs in Machine Learning-based Systems: A Faultload Benchmark
Although most of these tools use bugs' lifecycle, there is no standard benchmark of bugs to assess their performance, compare them and discuss their advantages and weaknesses.
Faults in Deep Reinforcement Learning Programs: A Taxonomy and A Detection Approach
To allow for the automatic detection of faults in DRL programs, we have defined a meta-model of DRL programs and developed DRLinter, a model-based fault detection approach that leverages static analysis and graph transformations.
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