  Translating natural language into Bash Commands is an emerging research field
that has gained attention in recent years. Most efforts have focused on
producing more accurate translation models. To the best of our knowledge, only
two datasets are available, with one based on the other. Both datasets involve
scraping through known data sources (through platforms like stack overflow,
crowdsourcing, etc.) and hiring experts to validate and correct either the
English text or Bash Commands. This paper provides two contributions to
research on synthesizing Bash Commands from scratch. First, we describe a
state-of-the-art translation model used to generate Bash Commands from the
corresponding English text. Second, we introduce a new NL2CMD dataset that is
automatically generated, involves minimal human intervention, and is over six
times larger than prior datasets. Since the generation pipeline does not rely
on existing Bash Commands, the distribution and types of commands can be custom
adjusted. We evaluate the performance of ChatGPT on this task and discuss the
potential of using it as a data generator. Our empirical results show how the
scale and diversity of our dataset can offer unique opportunities for semantic
parsing researchers.
