The Scalable Detection and Resolution of Data Clumps Using a Modular Pipeline with ChatGPT

This paper explores a modular pipeline architecture that integrates ChatGPT, a Large Language Model (LLM), to automate the detection and refactoring of data clumps—a prevalent type of code smell that complicates software maintainability. Data clumps refer to clusters of code that are often repeated and should ideally be refactored to improve code quality. The pipeline leverages ChatGPT’s capabilities to understand context and generate structured outputs, making it suitable for addressing complex software refactoring tasks. Through systematic experimentation, our study not only addresses the research questions outlined but also demonstrates that the pipeline can accurately identify data clumps, particularly excelling in cases that require semantic understanding—where localized clumps are embedded within larger codebases. While the solution significantly enhances the refactoring workflow, facilitating the management of distributed clumps across multiple files, it also presents challenges such as occasional compiler errors and high computational costs. Feedback from developers underscores the usefulness of LLMs in software development but also highlights the essential role of human oversight to correct inaccuracies. These findings demonstrate the pipeline’s potential to enhance software maintainability, offering a scalable and efficient solution for addressing code smells in real-world projects, and contributing to the broader goal of enhancing software maintainability in large-scale projects.