Abstract Context As Software Engineering (SE) practices evolve due to extensive increases in software size and complexity, the importance of tools to analyze and understand source code grows significantly. Objective This study aims to evaluate the abilities of Large Language Models (LLMs) in identifying DPs in source code, which can facilitate the development of better Design Pattern Recognition (DPR) tools. We compare the effectiveness of different LLMs in capturing semantic information relevant to the DPR task. Methods We studied Gang of Four (GoF) DPs from the P-MARt repository of curated Java projects. State-of-the-art language models, including Code2Vec, CodeBERT, CodeGPT, CodeT5, and RoBERTa, are used to generate embeddings from source code. These embeddings are then used for DPR via a k-nearest neighbors prediction. Precision, recall, and F1-score metrics are computed to evaluate performance. Results RoBERTa is the top performer, followed by CodeGPT and CodeBERT, which showed mean F1 Scores of 0.91, 0.79, and 0.77, respectively. The results show that LLMs without explicit pre-training can effectively store semantics and syntactic information, which can be used in building better DPR tools. Conclusion The performance of LLMs in DPR is comparable to existing state-of-the-art methods but with less effort in identifying pattern-specific rules and pre-training. Factors influencing prediction performance in Java files/programs are analyzed. These findings can advance software engineering practices and show the importance and abilities of LLMs for effective DPR in source code.

Requirements serve as the foundation for defining what a software product should accomplish, highlighting the importance of clear and well-written specifications [1]. Deficient requirements often lead to defects in delivered software, which can be challenging and costly to rectify [2].