39 citations
- American Institute of Aeronautics and AstronauticsUS2 papers
- Austrian Institute of TechnologyAT2 papers
- Centre National de la Recherche ScientifiqueFR2 papers
- KTH Royal Institute of TechnologySE2 papers
- Massachusetts Institute of TechnologyUS2 papers
- University of CopenhagenDK2 papers
- Aalto UniversityFI1 paper
- African Centre for Technology StudiesKE1 paper
- Austrian Academy of SciencesAT1 paper
- Budapest University of Technology and EconomicsHU1 paper
- Center for Integrated Quantum Science and TechnologyDE1 paper
- Czech Academy of Sciences, Institute of Organic Chemistry and BiochemistryCZ1 paper
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cs.SE2026
Data-aware Static Analysis: Improving Detection of Semantic Faults in Machine Learning Code Using Data Characteristics
Willem Meijer, Kristian Sandahl, Dániel Varró
Semantic faults specific to the use of machine learning models are a common problem for machine learning developers, causing suboptimal predictions, high computational cost, or inc…
cs.SE2026
JunoBench: A Benchmark Dataset of Crashes in Python Machine Learning Jupyter Notebooks
Yiran Wang, José Antonio Hernández López, José Antonio Hernández López +3
Jupyter notebooks are widely used for machine learning (ML) prototyping. Yet, few debugging tools are designed for ML code in notebooks, partly, due to the lack of benchmarks. We i…