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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.03755 |
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| _version_ | 1866912873584787456 |
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| author | Molina, Facundo Naziri, M M Abid Qin, Feiran Gorla, Alessandra d'Amorim, Marcelo |
| author_facet | Molina, Facundo Naziri, M M Abid Qin, Feiran Gorla, Alessandra d'Amorim, Marcelo |
| contents | Deep Learning (DL) libraries like TensorFlow and Pytorch simplify machine learning (ML) model development but are prone to bugs due to their complex design. Bug-finding techniques exist, but without precise API specifications, they produce many false alarms. Existing methods to mine API specifications lack accuracy. We explore using ML classifiers to determine input validity. We hypothesize that tensor shapes are a precise abstraction to encode concrete inputs and capture relationships of the data. Shape abstraction severely reduces problem dimensionality, which is important to facilitate ML training. Labeled data are obtained by observing runtime outcomes on a sample of inputs and classifiers are trained on sets of labeled inputs to capture API constraints. Our evaluation, conducted over 183 APIs from TensorFlow and Pytorch, shows that the classifiers generalize well on unseen data with over 91% accuracy. Integrating these classifiers into the pipeline of ACETest, a SoTA bug-finding technique, improves its pass rate from ~29% to ~61%. Our findings suggest that ML-enhanced input classification is an important aid to scale DL library testing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_03755 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Improving Deep Learning Library Testing with Machine Learning Molina, Facundo Naziri, M M Abid Qin, Feiran Gorla, Alessandra d'Amorim, Marcelo Software Engineering Deep Learning (DL) libraries like TensorFlow and Pytorch simplify machine learning (ML) model development but are prone to bugs due to their complex design. Bug-finding techniques exist, but without precise API specifications, they produce many false alarms. Existing methods to mine API specifications lack accuracy. We explore using ML classifiers to determine input validity. We hypothesize that tensor shapes are a precise abstraction to encode concrete inputs and capture relationships of the data. Shape abstraction severely reduces problem dimensionality, which is important to facilitate ML training. Labeled data are obtained by observing runtime outcomes on a sample of inputs and classifiers are trained on sets of labeled inputs to capture API constraints. Our evaluation, conducted over 183 APIs from TensorFlow and Pytorch, shows that the classifiers generalize well on unseen data with over 91% accuracy. Integrating these classifiers into the pipeline of ACETest, a SoTA bug-finding technique, improves its pass rate from ~29% to ~61%. Our findings suggest that ML-enhanced input classification is an important aid to scale DL library testing. |
| title | Improving Deep Learning Library Testing with Machine Learning |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2602.03755 |