  Pre-trained Code Language Models (Code-PLMs) have shown many advancements and
achieved state-of-the-art results for many software engineering tasks in the
past few years. These models are mainly targeted for popular programming
languages such as Java and Python, leaving out many other ones like R. Though R
has a wide community of developers and users, there is little known about the
applicability of Code-PLMs for R. In this preliminary study, we aim to
investigate the vulnerability of Code-PLMs for code entities in R. For this
purpose, we use an R dataset of code and comment pairs and then apply
CodeAttack, a black-box attack model that uses the structure of code to
generate adversarial code samples. We investigate how the model can attack
different entities in R. This is the first step towards understanding the
importance of R token types, compared to popular programming languages (e.g.,
Java). We limit our study to code summarization. Our results show that the most
vulnerable code entity is the identifier, followed by some syntax tokens
specific to R. The results can shed light on the importance of token types and
help in developing models for code summarization and method name prediction for
the R language.
