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Found 5 papers, 3 papers with code
From Informal to Formal -- Incorporating and Evaluating LLMs on Natural Language Requirements to Verifiable Formal Proofs
The research in AI-based formal mathematical reasoning has shown an unstoppable growth trend.
Enhancing Differential Testing With LLMs For Testing Deep Learning Libraries
However, existing differential testing techniques for deep learning (DL) libraries are limited by the key challenges of finding alternative implementations (called counterparts) for a given API and subsequently generating diverse test inputs.
COMET: Coverage-guided Model Generation For Deep Learning Library Testing
Techniques have been proposed to generate various DL models and apply them to test these libraries.
DeepFD: Automated Fault Diagnosis and Localization for Deep Learning Programs
Besides, for fault localization, DeepFD also outperforms the existing works, correctly locating 42% faulty programs, which almost doubles the best result (23%) achieved by the existing works.
SemMT: A Semantic-based Testing Approach for Machine Translation Systems
SemMT applies round-trip translation and measures the semantic similarity between the original and translated sentences.
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