KRONE derives semantic execution hierarchies from flat logs to enable modular multi-level anomaly detection with hybrid local and nested-aware detectors plus limited LLM use, delivering 10% F1 gains and over 100x data efficiency on benchmarks and industrial data.
Estimating the support of a high-dimensional distribution,
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Supervised models using 83 metrics achieve 0.85-0.9 recall for post-release Python faults, outperforming LLMs, with process metrics and code size most predictive and metrics plus embeddings capturing complementary information.
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KRONE: Scalable LLM-Augmented Log Anomaly Detection via Hierarchical Abstraction
KRONE derives semantic execution hierarchies from flat logs to enable modular multi-level anomaly detection with hybrid local and nested-aware detectors plus limited LLM use, delivering 10% F1 gains and over 100x data efficiency on benchmarks and industrial data.
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Will It Break in Production? Metric-Driven Prediction of Residual Defects in Python Systems
Supervised models using 83 metrics achieve 0.85-0.9 recall for post-release Python faults, outperforming LLMs, with process metrics and code size most predictive and metrics plus embeddings capturing complementary information.