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On the Relation between Code Quality and Machine Learning Performance: A Large-scale Empirical Study

arXiv · AI, language, vision and robotics · article · Sep 8, 2026 · UTC

Context: Computational notebooks are the standard environment for machine learning (ML) development. Within the ML community, model performance is often the primary considered metric, and code quality is treated as a secondary concern. This prioritization relies on a largely untested assumption that code quality and ML performance are unrelated. Practitioners also reuse existing code that may come from notebooks selected through social signals (popularity, author expertise) whose reliability as quality proxies has never been assessed. Objective: We empirically investigated the relationship bet

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