LLMs achieve only modest understanding of HMSC formal semantics at 52 percent accuracy, performing strongly on basic constructs but weakly on abstractions and traces.
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NeuroFlake integrates discriminative token mining into LLMs to classify flaky tests, raising F1-score to 69.34% on FlakeBench while showing greater robustness to semantic-preserving perturbations than prior methods.
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(How) Do Large Language Models Understand High-Level Message Sequence Charts?
LLMs achieve only modest understanding of HMSC formal semantics at 52 percent accuracy, performing strongly on basic constructs but weakly on abstractions and traces.
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NeuroFlake: A Neuro-Symbolic LLM Framework for Flaky Test Classification
NeuroFlake integrates discriminative token mining into LLMs to classify flaky tests, raising F1-score to 69.34% on FlakeBench while showing greater robustness to semantic-preserving perturbations than prior methods.