Code Smells for Machine Learning ApplicationsResearch Paper
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 24 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.
Tue 17 MayDisplayed time zone: Eastern Time (US & Canada) change
09:30 - 11:00 | AI SmellsCAIN 2022 at CAIN main room Chair(s): Ipek Ozkaya Carnegie Mellon Software Engineering Institute, Thomas Zimmermann Microsoft Research | ||
09:30 30mOther | Activity: Brainwriting CAIN 2022 | ||
10:00 15mResearch paper | Code Smells for Machine Learning ApplicationsResearch Paper CAIN 2022 Haiyin Zhang AI for Fintech Research, ING, Luís Cruz Deflt University of Technology, Arie van Deursen Delft University of Technology, Netherlands Pre-print | ||
10:15 15mResearch paper | Data Smells: Categories, Causes and Consequences, and Detection of Suspicious Data in AI-based SystemsResearch Paper CAIN 2022 Harald Foidl University of Innsbruck, Michael Felderer University of Innsbruck, Rudolf Ramler Software Competence Center Hagenberg Pre-print | ||
10:30 15mResearch paper | Data smells in Public DatasetsResearch Paper CAIN 2022 Arumoy Shome Delft University of Technology, Luís Cruz Deflt University of Technology, Arie van Deursen Delft University of Technology, Netherlands Pre-print | ||
10:45 15mOther | Discussion on Smells in AI CAIN 2022 |