Refactoring Deep Learning Code: A Study of Practices and Unsatisfied Tool Needs

TL;DR

This paper empirically examines code refactoring practices in deep learning projects, revealing unique patterns, practitioners' perspectives, and unmet tool needs, highlighting the importance of tailored refactoring tools for deep learning software.

Contribution

It provides the first detailed analysis of refactoring practices in deep learning code, compares them with traditional software, and surveys practitioners to identify tool gaps and future directions.

Findings

Refactoring operation types differ from traditional software.

Practitioners see refactoring tools as crucial but inadequate.

Current tools do not fully support deep learning code refactoring.

Abstract

With the rapid development of deep learning, the implementation of intricate algorithms and substantial data processing have become standard elements of deep learning projects. As a result, the code has become progressively complex as the software evolves, which is difficult to maintain and understand. Existing studies have investigated the impact of refactoring on software quality within traditional software. However, the insight of code refactoring in the context of deep learning is still unclear. This study endeavors to fill this knowledge gap by empirically examining the current state of code refactoring in deep learning realm, and practitioners' views on refactoring. We first manually analyzed the commit history of five popular and well-maintained deep learning projects (e.g., PyTorch). We mined 4,921 refactoring practices in historical commits and measured how different types and elements of refactoring operations are distributed and found that refactoring operation types' distribution in deep learning projects is different from it in traditional Java software. We then surveyed 159 practitioners about their views of code refactoring in deep learning projects and their expectations of current refactoring tools. The result of the survey showed that refactoring research and the development of related tools in the field of deep learning are crucial for improving project maintainability and code quality, and that current refactoring tools do not adequately meet the needs of practitioners. Lastly, we provided our perspective on the future advancement of refactoring tools and offered suggestions for developers' development practices.