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.
Cross-project defect prediction via trans- fer learning: A benchmark study
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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.