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Studying Vulnerable Code Entities in R

arXiv 2402.04421 ↗First posted 6 Feb 2024cs.SE cs.AIcited by 0

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Abstract (arXiv)

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.

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References · 19

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7 more
  1. 2022CodeAttack: Code-Based Adversarial Attacks for Pre-trained Programming Language Modelscited by 53
  2. 2021Multilingual training for Software Engineeringcited by 43
  3. 2020A Closer Look at the Robustness of Vision-and-Language Pre-trained Modelscited by 37
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  5. 2021Adversarial Robustness of Deep Code Comment Generationcited by 22
  6. 2023Characterizing Bugs in Python and R Data Analytics Programscited by 3
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