arXiv:2509.20491v3 Announce Type: replace-cross Abstract: Machine Learning (ML) pipelines encode quality-relevant decisions across data preparation, training, evaluation, and configuration code. Some recurring source-level quality problems in these pipelines, known as ML code smells, may not cause immediate failures but can harm reproducibility, robustness, efficiency, or maintainability. Detecting ML code smell occurrences is challenging because the decisive evidence is often non-local, spanning helper functions, wrappers, imports, control-flow, and data-flow relations. We present SpecDetect4
The increasing complexity and adoption of ML systems necessitate more robust tools for quality assurance, pushing research into automated detection of quality issues like code smells.
Ensuring the reproducibility, robustness, and maintainability of ML models is crucial for their deployment in critical applications and for the efficiency of ML development workflows.
New methods leveraging code property graphs will enable more effective and automated identification of complex, non-local ML code smells, improving the underlying reliability of ML software.
- · ML developers
- · ML platform providers
- · Organizations deploying ML at scale
- · Software quality assurance sector
- · Organizations with low ML quality standards
- · Manual code review processes
Improved software quality and reduced debugging time in ML development pipelines.
Faster deployment of more reliable AI systems across various industries due to higher trust in ML code.
Increased public and regulatory confidence in AI applications, potentially accelerating their societal integration and leading to new compliance standards for ML code quality.
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Read at arXiv cs.AI