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Sun Yat-sen University, Zhuhai, China
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Even the top-performing LLM struggles with cross-file reasoning, achieving only 69.1% accuracy on a new benchmark designed to reflect real-world software development challenges.
Log-based anomaly detection models are missing 90% of the picture, but AnomalyGen uses LLMs and static analysis to hallucinate realistic training data and close the gap.
LLMs can now add features to entire codebases with 36% higher success by generating multiple design options and validating their architectural impact.
