An Empirical Comparison of Pre-Trained Models of Source Code

Changan Niu; Chuanyi Li; Vincent Ng; Dongxiao Chen; Jidong Ge; Bin Luo

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arXiv · 2302.04026

An Empirical Comparison of Pre-Trained Models of Source Code

Changan Niu·Chuanyi Li·Vincent Ng·Dongxiao Chen·Jidong Ge·Bin Luo

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Abstract

While a large number of pre-trained models of source code have been successfully developed and applied to a variety of software engineering (SE) tasks in recent years, our understanding of these pre-trained models is arguably fairly limited. With the goal of advancing our understanding of these models, we perform the first systematic empirical comparison of 19 recently-developed pre-trained models of source code on 13 SE tasks. To gain additional insights into these models, we adopt a recently-developed 4-dimensional categorization of pre-trained models, and subsequently investigate whether there are correlations between different categories of pre-trained models and their performances on different SE tasks.

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Changan Niu, Chuanyi Li, Vincent Ng, Dongxiao Chen, Jidong Ge, Bin Luo. 2023-02-08. An Empirical Comparison of Pre-Trained Models of Source Code. https://arxiv.org/abs/2302.04026

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