# MLScent A tool for Anti-pattern detection in ML projects

arXiv: https://arxiv.org/abs/2502.18466 · HTML: https://mlsanity.com/paper/2502.18466/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 41 references that are in the map.
- First posted on arXiv 30 Jan 2025.

## Builds on (most cited first, 7 of 7)

- [Snorkel: Rapid Training Data Creation with Weak Supervision](https://mlsanity.com/paper/1711.10160/) (2017)
- [Machine Learning Testing: Survey, Landscapes and Horizons](https://mlsanity.com/paper/1906.10742/) (2019)
- [The Prevalence of Code Smells in Machine Learning projects](https://mlsanity.com/paper/2103.04146/) (2021)
- [Code Smells for Machine Learning Applications](https://mlsanity.com/paper/2203.13746/) (2022)
- [Deep Learning Anti-patterns from Code Metrics History](https://mlsanity.com/paper/1910.07658/) (2019)
- ["Project smells" -- Experiences in Analysing the Software Quality of ML Projects with mllint](https://mlsanity.com/paper/2201.08246/) (2022)
- [When Code Smells Meet ML: On the Lifecycle of ML-specific Code Smells in ML-enabled Systems](https://mlsanity.com/paper/2403.08311/) (2024)

## Built on by (most cited first, 2 of 2)

- [scicode-lint: Detecting Methodology Bugs in Scientific Python Code with LLM-Generated Patterns](https://mlsanity.com/paper/2603.17893/) (2026)
- [Data-aware Static Analysis: Improving Detection of Semantic Faults in Machine Learning Code Using Data Characteristics](https://mlsanity.com/paper/2606.09957/) (2026)

## Abstract (arXiv)

Machine learning (ML) codebases face unprecedented challenges in maintaining code quality and sustainability as their complexity grows exponentially. While traditional code smell detection tools exist, they fail to address ML-specific issues that can significantly impact model performance, reproducibility, and maintainability. This paper introduces MLScent, a novel static analysis tool that leverages sophisticated Abstract Syntax Tree (AST) analysis to detect anti-patterns and code smells specific to ML projects. MLScent implements 76 distinct detectors across major ML frameworks including TensorFlow (13 detectors), PyTorch (12 detectors), Scikit-learn (9 detectors), and Hugging Face (10 detectors), along with data science libraries like Pandas and NumPy (8 detectors each). The tool's architecture also integrates general ML smell detection (16 detectors), and specialized analysis for data preprocessing and model training workflows. Our evaluation demonstrates MLScent's effectiveness through both quantitative classification metrics and qualitative assessment via user studies feedback with ML practitioners. Results show high accuracy in identifying framework-specific anti-patterns, data handling issues, and general ML code smells across real-world projects.