Using a corpus of 5542 fault-injected traces from 38 DL programs, the study finds a 0.19 balanced accuracy gap in fault diagnosis between within-program and cross-program evaluation caused by program-specific feature structures.
Refty: refinement types for valid deep learning models,
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
fields
cs.SE 4years
2026 4verdicts
UNVERDICTED 4representative citing papers
Proposes data-aware static analysis combining data/control flow and API contracts to detect semantic faults in ML code early, shown on sample real-world notebooks.
dille detects silent semantic faults in random forest ML pipelines with 91% precision via data-informed static analysis on Kaggle notebooks, finding 12-18% of scripts affected.
WALL-E uses external library linking via client-server architecture to support ten managed languages in WebAssembly with hundreds-fold speedup over nested runtimes.
citing papers explorer
-
Evaluation-Strategy Gap in Fault Diagnosis of Deep Learning Programs
Using a corpus of 5542 fault-injected traces from 38 DL programs, the study finds a 0.19 balanced accuracy gap in fault diagnosis between within-program and cross-program evaluation caused by program-specific feature structures.
-
Data-aware Static Analysis: Improving Detection of Semantic Faults in Machine Learning Code Using Data Characteristics
Proposes data-aware static analysis combining data/control flow and API contracts to detect semantic faults in ML code early, shown on sample real-world notebooks.
-
Are We Lost in the Woods? Detecting Silent Semantic Faults for Random Forest Classifiers with Data-informed Static Analysis
dille detects silent semantic faults in random forest ML pipelines with 91% precision via data-informed static analysis on Kaggle notebooks, finding 12-18% of scripts affected.
-
Bringing Managed Language Support to WebAssembly with External Library Linking
WALL-E uses external library linking via client-server architecture to support ten managed languages in WebAssembly with hundreds-fold speedup over nested runtimes.