TLDR: A large-scale study used LLMs to analyze refactoring motivations in 114 Java open-source projects. It found that LLMs agreed with human judgment 80% of the time but only matched existing literature motivations in 47% of cases, often providing richer, more detailed explanations (22% of cases) or entirely new rationales (25%). Most refactoring was driven by pragmatic needs like clarity and simplification. While developer experience and code readability metrics were important predictors for AI models, their direct correlation with motivations was weak, suggesting hybrid AI-metric systems are needed for effective refactoring recommendations.
Code refactoring, the process of restructuring existing computer code without changing its external behavior, is a cornerstone of maintaining software quality. Despite its well-known benefits, such as improved design, readability, and maintainability, it often comes with significant costs in terms of time and resources. Understanding the underlying motivations that drive developers to refactor code is crucial for making this process more effective and widely adopted.
A recent large-scale empirical study titled “What Were You Thinking? An LLM-Driven Large-Scale Study of Refactoring Motivations in Open-Source Projects” delved into this very question. Researchers Mikel Robredo, Matteo Esposito, Fabio Palomba, Rafael Peñaloza, and Valentina Lenarduzzi leveraged Large Language Models (LLMs) to analyze developers’ refactoring activities in open-source projects, comparing their findings with existing literature and exploring the role of software metrics.
The Study’s Approach
The research team conducted an extensive study involving 114 Java projects hosted on GitHub. They analyzed over 13 million refactoring activities across more than 783,000 commit histories. The core of their method involved using advanced LLMs to interpret the motivations developers expressed in their version control data, specifically from commit messages and code differences. This LLM-driven approach was then compared against human judgment and previously reported motivations in software engineering literature.
Key Insights from AI Analysis
One of the most striking findings was the LLMs’ ability to align with human judgment. In approximately 80% of cases, the AI models’ identified motivations matched those of human experts. However, when compared to motivations already documented in literature, the alignment dropped to 47%. This discrepancy suggests that while LLMs can accurately interpret immediate developer intent, they often provide a different perspective than what has been formally categorized in past studies.
Interestingly, the LLMs didn’t just replicate existing knowledge; they enriched it. In 22% of cases, the models provided more detailed rationales, often highlighting aspects like readability, clarity, and structural improvements that were implicit or less emphasized in prior work. Furthermore, 25% of the motivations identified by the LLMs were entirely new, hinting at previously uncaptured developer rationales.
Pragmatism Over Principles
The study revealed a predominantly pragmatic intent behind refactoring. Over 55% of all identified motivations centered around improving code clarity and readability or simplifying and reducing redundancy. This indicates that developers often refactor for immediate, tangible benefits like making code easier to understand or removing unnecessary complexity, rather than strictly adhering to abstract design principles or long-term architectural goals. This pragmatic approach aligns with observations of developer behavior in real-world scenarios.
The Role of Software Metrics
The researchers also investigated how software metrics—measurable characteristics of code and development processes—relate to refactoring motivations. While metrics related to developer experience and code readability consistently ranked high in importance for machine learning models trying to predict refactoring motivations, their direct statistical correlation with specific motivation categories was found to be weak. This suggests that metrics alone, while useful as signals, lack the semantic richness to fully explain *why* developers refactor.
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Implications for the Future of Software Development
The study concludes that LLMs are effective at capturing surface-level motivations and providing localized explanations for refactoring. However, they struggle with deeper architectural reasoning, which often requires a more holistic understanding of a project’s long-term goals. The value of LLMs, therefore, lies in their ability to offer detailed, context-specific justifications, especially when combined with software metrics in what the authors call “hybrid approaches.”
For researchers, this opens avenues for exploring how to provide LLMs with richer architectural and historical context. For practitioners, LLMs can serve as valuable assistants in documenting and reviewing code changes, making the “why” behind refactoring more explicit and shareable. This could lead to more systematic refactoring prioritization, balancing short-term improvements with long-term architectural goals.
To dive deeper into the methodology and detailed findings, you can read the full research paper here.
