  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.
  In this work, we analyze over a hundred of pre-trained and fine-tuned models.
Our results show that 1) multilingual PLMs have a lower Performance-to-Time
Ratio (the BLEU, METEOR, or MRR scores over the fine-tuning duration) as
compared to monolingual PLMs, 2) our proposed strategy to select target
programming languages to fine-tune multilingual PLMs is effective: it reduces
the time to fine-tune yet achieves higher performance in Code Summarization and
Code Search tasks, and 3) our proposed strategy consistently shows good
performance on different code lengths.
