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TAILOR, a log parsing framework that improves template inference for rare log groups through template-preserving augmentation, is proposed and experimental results show that TAILOR improves parsing accuracy on rare log groups by 19% over the strongest baseline while maintaining competitive performance on complete datasets.
AutoGlue achieves a 58.7% improvement in API F1 scores, demonstrating that LLMs can seamlessly translate natural-language requirements into executable code.
LLMs are surprisingly bad at fixing real-world logging security vulnerabilities, despite being moderately effective at detecting them.