Beiqi Zhang

Beiqi Zhang, Peng Liang, Xin Zhou et al.

Automated code smell detection faces persistent challenges due to the subjectivity of heuristic rules and the limited performance of traditional ML/DL models. While Large Language Models (LLMs) offer a promising alternative, their adoption is impeded by high fine-tuning costs and a lack of "LM-ready" benchmarks. To bridge these gaps, we present a study with two synergistic contributions. First, we constructed a high-quality benchmark for Complex Conditional, Complex Method, Feature Envy, and Data Class, validated through a rigorous two-stage manual review. Second, leveraging this benchmark, we systematically evaluated four Parameter-Efficient Fine-Tuning (PEFT) methods across nine LMs of varying parameter sizes. Their performance is compared against a comprehensive suite of baselines, including heuristics-based detectors, Deep Learning (DL)-based approaches, and state-of-the-art general-purpose LLMs under multiple In-Context Learning (ICL) settings. Our results demonstrate that PEFT methods achieve effectiveness comparable to or surpassing full fine-tuning while substantially reducing peak GPU memory usage for code smell detection. Furthermore, PEFT-tuned LMs consistently outperform all baselines, yielding MCC improvements ranging from 0.33% to 13.69%, with particularly notable gains for specific smell categories. These findings highlight PEFT techniques as effective and scalable solutions for advancing code smell detection.

Beiqi Zhang, Peng Liang, Qiong Feng et al.

As one of the most popular dynamic languages, Python experiences a decrease in readability and maintainability when code smells are present. Recent advancements in Large Language Models have sparked growing interest in AI-enabled tools for both code generation and refactoring. GitHub Copilot is one such tool that has gained widespread usage. Copilot Chat, released in September 2023, functions as an interactive tool aimed at facilitating natural language-powered coding. However, limited attention has been given to understanding code smells in Copilot-generated Python code and Copilot Chat's ability to fix the code smells. To this end, we built a dataset comprising 102 code smells in Copilot-generated Python code. Our aim is to first explore the occurrence of code smells in Copilot-generated Python code and then evaluate the effectiveness of Copilot Chat in fixing these code smells employing different prompts. The results show that 8 out of 10 types of code smells can be detected in Copilot-generated Python code, among which Multiply-Nested Container is the most common one. For these code smells, Copilot Chat achieves a highest fixing rate of 87.1%, showing promise in fixing Python code smells generated by Copilot itself. In addition, the effectiveness of Copilot Chat in fixing these smells can be improved by providing more detailed prompts.