Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

5-2022

Abstract

A recent study by Ahmed and Devanbu reported that using a corpus of code written in multilingual datasets to fine-tune multilingual Pre-trained Language Models (PLMs) achieves higher performance as opposed to using a corpus of code written in just one programming language. However, no analysis was made with respect to fine-tuning monolingual PLMs. Furthermore, some programming languages are inherently different and code written in one language usually cannot be interchanged with the others, i.e., Ruby and Java code possess very different structure. To better understand how monolingual and multilingual PLMs affect different programming languages, we investigate 1) the performance of PLMs on Ruby for two popular Software Engineering tasks: Code Summarization and Code Search, 2) the strategy (to select programming languages) that works well on fine-tuning multilingual PLMs for Ruby, and 3) the performance of the fine-tuned PLMs on Ruby given different code lengths.

Keywords

Pre-trained language models, Low-resource languages

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of the 30th IEEE/ACM International Conference on Program Comprehension, ICPC 2022, Virtual Event, May 16-17

First Page

401

Last Page

412

Identifier

10.1145/3524610.3527917

Publisher

Association for Computing Machinery

City or Country

New York

Additional URL

https://doi.org/10.1145/3524610.3527917

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