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. 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 avoiding issues in the long run. To help improve the machine learning code quality, we conducted two studies in this thesis. The first study 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 second study aims to develop a tool to improve code quality and study the prevalence of machine learning-specific code smells. We extend a static analysis tool dslinter and run it on both Python notebook datasets and regular Python project datasets. Moreover, we analyse the result to check the tool's validity and investigate the code smell prevalence in machine learning applications. The code smell catalog and dslinter together help data scientists and developers produce and maintain high-quality machine learning application code.

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. ACM Reference Format: Haiyin Zhang, Luís Cruz, and Arie van Deursen. 2022. Code Smells for Machine Learning Applications. In 1st Conference on AI Engineering - Software Engineering for AI (CAIN'22), May 16-24, 2022, Pittsburgh, PA, USA. ACM, New York, NY, USA, 12 pages. https://doi.org/10.1145/3522664.3528620

@en