Are We Lost in the Woods? Detecting Silent Semantic Faults for Random Forest Classifiers with Data-informed Static Analysis

2026-06-05

AI Summary

This work addresses the problem of resource wastage in random forest classifiers caused by semantic errors—such as those stemming from data imbalance—that often remain undetected until after model training. To mitigate this, the authors propose a data-driven static analysis method that operates without access to the original dataset. By modeling machine learning scripts as directed acyclic graphs and integrating formal API contracts with aggregated data properties, the approach enables early detection of silent semantic errors even in confidential environments. The resulting open-source tool, dille, demonstrates high efficacy on real-world Kaggle notebooks, identifying such errors in 12%–18% of random forest scripts with 91% precision and sub-second analysis overhead.

Abstract

While machine learning (ML) software necessitates effective quality assurance, ML engineers still encounter silent semantic faults, such as imbalanced datasets, that degrade prediction performance without apparent symptoms. These faults are typically detected after expensive training cycles, causing significant resource waste. We propose a data-informed static analysis technique to detect silent semantic faults in ML scripts that use the popular random forest classifier. Our approach extracts ML pipelines into directed acyclic graphs and evaluates them against formalized API contracts to detect structural, data, and hyperparameter faults. Our analysis uses aggregated data properties, enabling fault detection even when datasets are inaccessible due to confidentiality restrictions. We implemented this technique in an open-source tool, dille, and evaluated it on real-world Kaggle notebooks that use the random forest classifier. Our results demonstrate that the tool identifies relevant semantic faults with 91% precision and sub-second runtime overhead, making it suitable for integration into integrated development environments, agentic workflows, and continuous integration pipelines. Our empirical study reveals that 12% to 18% of existing ML notebooks that use the random forest classifier are affected by silent semantic faults, highlighting the immediate practical utility of data-informed static analysis in reducing the burden of ML debugging.

Problem

• silent semantic faults
• random forest classifiers
• machine learning debugging
• data imbalance
• ML quality assurance

Innovation

• data-informed static analysis
• silent semantic faults
• random forest classifier
• ML pipeline verification
• API contracts