Introduction
In software engineering, code refactoring is a core component of maintaining the long-term maintainability and stability of a system. Traditional refactoring relies on developer experience and toolchain support, resulting in limited efficiency and difficulty adapting to the evolving complexities of business logic. With the rise of large language models (LLMs), ChatGPT, an intelligent tool capable of understanding and generating natural and programming languages, offers unprecedented possibilities for code refactoring.
This article will explore four perspectives: related research, task definition and problem setting, practical application and discussion, and limitations and future work. It aims to provide a clear picture for both academia and industry: what ChatGPT can do in code refactoring, how it is done, to what extent it can be achieved, and its future development direction.
I. Related Work
Code refactoring, an engineering practice that improves the internal structure of code without changing its external behavior, was first proposed and systematically summarized by Martin Fowler et al. In the traditional software development lifecycle, refactoring typically relies on manual analysis and semi-automated tool support. For example, IDEs such as Eclipse and IntelliJ IDEA provide common refactoring operations such as method extraction and variable renaming. However, most of these tools are based on static analysis, struggle to understand complex business context, and can only perform a limited number of refactorings.
In recent years, with the advancement of deep learning technology, the integration of natural language processing (NLP) and program analysis has given rise to the field of "code intelligence." Models such as CodeBERT, GraphCodeBERT, and Codex, pre-trained on large-scale code corpora, have demonstrated the ability to automatically generate, complete, and translate code. In this context, ChatGPT, a conversational model based on the GPT architecture, has been widely used for tasks such as code summarization, unit test generation, bug fixing, and code optimization due to its enhanced semantic understanding and cross-language transfer capabilities.
Previous research has demonstrated ChatGPT's potential in both structured and unstructured tasks. For example, in "Text Summarization Using Large Language Models: A Comparative Study of MPT-7b-instruct, Falcon-7b-instruct, and OpenAI ChatGPT Models," the authors highlight ChatGPT's performance in cross-domain text abstraction and refactoring tasks. Similarly, code refactoring tasks also require optimizing code structure while maintaining functional equivalence, thus forming a corresponding relationship with text refactoring.
Further related research focuses on the following areas:
Program transformation and translation: Using LLM to transform inefficient or redundant code into clearer and more efficient implementations.
Improving readability and maintainability: Leveraging model suggestions to improve naming, comments, and modular structure.
Integrating automated testing and verification: Introducing test case generation and verification mechanisms during the refactoring process to ensure functionality is not compromised.
Cross-language code migration: The model supports migration and refactoring from one programming language to another.
As can be seen, the addition of ChatGPT not only breaks through the limitations of traditional tools but also expands code refactoring to a more intelligent and flexible dimension. However, its application is still in the exploratory stage, lacking unified standards and a systematic evaluation framework. This also paves the way for further research.
II. Task Definition and Problem Setting
Before discussing the application of ChatGPT, it is necessary to clarify the task definition of "code refactoring" within the model context. The traditional definition emphasizes improving the internal structure of code without changing its external behavior. Within the LLM framework, we can expand this to guiding the model through natural language interaction to generate semantically equivalent but higher-quality code.
Task Definition
Input: A source code to be refactored (possibly accompanied by contextual explanations, functional descriptions, or test cases).
Output: A target code with the same functionality, a clearer structure, and improved maintainability.
Constraints:
Ensure semantic equivalence, meaning consistency in functionality and logic;
Improvement dimensions include readability, efficiency, modularity, and appropriate use of design patterns;
Support for compatibility with different programming languages and cross-platforms.
Problem Setting
Model-driven code refactoring addresses the following core issues:
Semantic Preservation: How can we ensure that the code generated by the model is fully consistent with the original functionality?
Context Understanding: How can the model effectively utilize the code context and natural language explanations?
Evaluation: How can we establish a unified metric system to evaluate the quality of the refactored code generated by the model?
Interaction Issues: During the refactoring process, how can we design the interaction between users and the model to improve the controllability of the results?
Research Difficulties
Uncertainty in the Natural Language to Code Mapping: Different descriptions can lead to multiple implementations.
Code Size and Complexity: Refactoring long functions or cross-module code requires the model to be able to model long-range dependencies.
Cross-Language and Cross-Paradigm Refactoring: Differences between different programming paradigms (object-oriented, functional) pose challenges to the model.
The above task definition and problem setting provide clear boundaries and research objectives for subsequent empirical research and experimental design.
III. Discussion
The discussion on the application of ChatGPT in code refactoring can be centered around the following dimensions:
Advantages
Natural Language Driven: Developers can describe requirements in natural language and quickly receive refactoring suggestions.
Contextual Adaptability: The model can combine comments, documentation, and the code itself for comprehensive analysis.
Cross-Domain Knowledge Transfer: Leveraging training experience on massive data sets, the model can identify and recommend common design patterns.
Practical Examples
In Python code refactoring, ChatGPT can automatically detect duplicate logic and extract it into functions. In Java projects, the model can recommend appropriate interface abstractions to reduce coupling. These applications demonstrate the model's strong potential for local and structural optimization.
Potential Risks
Semantic Bias: The code generated by the model may deviate from the original logic under extreme conditions.
Security Vulnerabilities: Incorrect refactorings may introduce security vulnerabilities, such as resource leaks or injection attacks.
Data Dependency: Model performance is limited by the coverage and quality of the training data.
Evaluation and Benchmarking Needs
Currently, the evaluation of code refactoring remains at the case verification stage and lacks universally applicable metrics such as BLEU and ROUGE for NLP tasks. Future research should explore new evaluation criteria, such as code maintainability indices, execution efficiency testing, and automated verification frameworks.
In summary, the discussion highlights the opportunities and challenges of ChatGPT in application scenarios, laying the foundation for a deeper analysis of its limitations and future work.
IV. Limitations and Future Work
Although ChatGPT demonstrates great potential for code refactoring, its application still faces significant limitations:
Lack of Controllability
ChatGPT's output is generatively random, resulting in different results for the same input. This uncertainty makes the refactoring results unstable.
Context Window Limitation
When working with large-scale projects, the model may not be able to load sufficient contextual information at once, resulting in limited effectiveness for cross-file or cross-module refactoring.
Lack of Explainability
When the model provides refactoring suggestions, it often fails to clearly explain the rationale behind its choices, reducing developer trust.
Lack of Verification Mechanism
Current refactorings rely heavily on manual user verification and lack deep integration with automated testing and static analysis tools.
Future Work
Model Architecture Improvement
Integrate the Code Syntax Tree (AST) and Semantic Graph (CFG) to enhance the model's structural understanding and reasoning capabilities.
Interactive Refactoring System
Build a human-computer collaborative framework that enables developers to dynamically correct and guide the model generation process.
Establish an Evaluation Metric System
Propose unified refactoring quality standards, such as code maintainability scores, complexity reduction rates, and execution efficiency improvements.
Cross-Language and Cross-Paradigm Exploration
Research how to leverage models for cross-language transferable refactoring and address mapping issues between different programming paradigms.
Security and Compliance
Introduce security review mechanisms into model refactoring output to ensure compliance with industry standards and regulatory requirements.
These directions will not only help address existing shortcomings but also promote the systematic application of LLM in software engineering.
V. Conclusion
In summary, ChatGPT has ushered in a new paradigm shift in the field of code refactoring. It transcends the limitations of traditional tools and, leveraging natural language understanding and cross-language transfer capabilities, provides a novel approach to improving code quality, maintainability, and development efficiency. However, it still faces shortcomings in controllability, explainability, and verification mechanisms. Future research should focus on establishing a unified evaluation system, enhancing the model's structural reasoning capabilities, and promoting the construction of human-computer collaborative frameworks. As these challenges are gradually addressed, ChatGPT is expected to play a more central role in software engineering practice and become a key driver of intelligent code refactoring.
References
Fowler, M. (1999). Refactoring: Improving the Design of Existing Code. Addison-Wesley.
Chen, M., et al. (2021). Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374.
Ahmad, W. U., Chakraborty, S., Ray, B., & Chang, K. W. (2021). Unified pre-training for program understanding and generation. NAACL-HLT.
Li, Y., Wang, S., & Xie, T. (2022). Automated code refactoring: A review of related techniques. Journal of Systems and Software.
Text Summarization with Large Language Models: A Comparative Study of MPT-7b-instruct, Falcon-7b-instruct, and OpenAI ChatGPT Models, 2023.
Large Language Models in VNHSGE Performance Comparison on English Datasets: OpenAI ChatGPT, Microsoft Bing Chat, and Google Bard, 2023.
