# Code Smells for Machine Learning Applications

arXiv: https://arxiv.org/abs/2203.13746 · HTML: https://mlsanity.com/paper/2203.13746/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 58 references that are in the map.
- First posted on arXiv 25 Mar 2022.

## Builds on (most cited first, 6 of 6)

- [Taxonomy of Real Faults in Deep Learning Systems](https://mlsanity.com/paper/1910.11015/) (2019)
- [A Comprehensive Study on Deep Learning Bug Characteristics](https://mlsanity.com/paper/1906.01388/) (2019)
- [AI Lifecycle Models Need To Be Revised. An Exploratory Study in Fintech](https://mlsanity.com/paper/2010.02716/) (2020)
- [Code Smells and Refactoring: A Tertiary Systematic Review of Challenges and Observations](https://mlsanity.com/paper/2004.10777/) (2020)
- [The Prevalence of Code Smells in Machine Learning projects](https://mlsanity.com/paper/2103.04146/) (2021)
- [A large-scale comparative analysis of Coding Standard conformance in Open-Source Data Science projects](https://mlsanity.com/paper/2007.08978/) (2020)

## Built on by (most cited first, 8 of 8)

- [PeaTMOSS: A Dataset and Initial Analysis of Pre-Trained Models in Open-Source Software](https://mlsanity.com/paper/2402.00699/) (2024)
- [MLScent A tool for Anti-pattern detection in ML projects](https://mlsanity.com/paper/2502.18466/) (2025)
- [Optimizing Token Consumption in LLMs: A Nano Surge Approach for Code Reasoning Efficiency](https://mlsanity.com/paper/2504.15989/) (2025)
- [PeaTMOSS: Mining Pre-Trained Models in Open-Source Software](https://mlsanity.com/paper/2310.03620/) (2023)
- [SpecDetect4ML: Detecting Non-Local ML Code Smells with Code Property Graphs](https://mlsanity.com/paper/2509.20491/) (2025)
- [Analysing Python Machine Learning Notebooks with Moose](https://mlsanity.com/paper/2509.11748/) (2025)
- [LLM Code Smells: A Taxonomy and Detection Approach](https://mlsanity.com/paper/2605.22976/) (2026)
- [From AI Technical Debt to Agentic Technical Debt: A Systematic Mapping of Root Causes and Manifestations in Agentic AI Systems](https://mlsanity.com/paper/2608.01001/) (2026)

## Abstract (arXiv)

The popularity of machine learning has wildly expanded in recent years. Machine learning techniques have been heatedly studied in academia and applied in the industry to create business value. However, there is a lack of guidelines for code quality in machine learning applications. In particular, code smells have rarely been studied in this domain. Although machine learning code is usually integrated as a small part of an overarching system, it usually plays an important role in its core functionality. Hence ensuring code quality is quintessential to avoid issues in the long run. This paper proposes and identifies a list of 22 machine learning-specific code smells collected from various sources, including papers, grey literature, GitHub commits, and Stack Overflow posts. We pinpoint each smell with a description of its context, potential issues in the long run, and proposed solutions. In addition, we link them to their respective pipeline stage and the evidence from both academic and grey literature. The code smell catalog helps data scientists and developers produce and maintain high-quality machine learning application code.