# LLM-Based Design Pattern Detection

arXiv: https://arxiv.org/abs/2502.18458 · HTML: https://mlsanity.com/paper/2502.18458/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 23 references that are in the map.
- First posted on arXiv 25 Feb 2025.

## Builds on (most cited first, 6 of 6)

- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [A Bibliometric Review of Large Language Models Research from 2017 to 2023](https://mlsanity.com/paper/2304.02020/) (2023)
- [Generative Pre-Trained Transformer for Design Concept Generation: An Exploration](https://mlsanity.com/paper/2111.08489/) (2021)
- [Feature Maps: A Comprehensible Software Representation for Design Pattern Detection](https://mlsanity.com/paper/1812.09873/) (2018)
- [Feature-Based Software Design Pattern Detection](https://mlsanity.com/paper/2012.01708/) (2020)
- [GEML: A Grammar-based Evolutionary Machine Learning Approach for Design-Pattern Detection](https://mlsanity.com/paper/2401.07042/) (2024)

## Abstract (arXiv)

Detecting design pattern instances in unfamiliar codebases remains a challenging yet essential task for improving software quality and maintainability. Traditional static analysis tools often struggle with the complexity, variability, and lack of explicit annotations that characterize real-world pattern implementations. In this paper, we present a novel approach leveraging Large Language Models to automatically identify design pattern instances across diverse codebases. Our method focuses on recognizing the roles classes play within the pattern instances. By providing clearer insights into software structure and intent, this research aims to support developers, improve comprehension, and streamline tasks such as refactoring, maintenance, and adherence to best practices.