The potential of LLMs for coding with low-resource and domain-specific
  programming languages
Artur Tarassow
This paper presents a study on the feasibility of using large language models
(LLM) for coding with low-resource and domain-specific programming languages
that typically lack the amount of data required for effective LLM processing
techniques. This study focuses on the econometric scripting language named
hansl of the open-source software gretl and employs a proprietary LLM based on
GPT-3.5. Our findings suggest that LLMs can be a useful tool for writing,
understanding, improving, and documenting gretl code, which includes generating
descriptive docstrings for functions and providing precise explanations for
abstract and poorly documented econometric code. While the LLM showcased
promoting docstring-to-code translation capability, we also identify some
limitations, such as its inability to improve certain sections of code and to
write accurate unit tests. This study is a step towards leveraging the power of
LLMs to facilitate software development in low-resource programming languages
and ultimately to lower barriers to entry for their adoption.