  In today's software world with its cornucopia of reusable software libraries,
when a programmer is faced with a programming task that they suspect can be
completed through the use of a library, they often look for code examples using
a search engine and then manually adapt found examples to their specific
context of use. We put forward a vision based on a new breed of developer tools
that have the potential to largely automate this process. The key idea is to
adapt code autocompletion tools such that they take into account not only the
developer's already-written code but also the intent of the task the developer
is trying to achieve next, formulated in plain natural language. We call this
practice of enriching the code with natural language intent to facilitate its
completion natural language-guided programming.
  To show that this idea is feasible we design, implement and benchmark a tool
that solves this problem in the context of a specific domain (data science) and
a specific programming language (Python). Central to the tool is the use of
language models trained on a large corpus of documented code. Our initial
experiments confirm the feasibility of the idea but also make it clear that we
have only scratched the surface of what may become possible in the future. We
end the paper with a comprehensive research agenda to stimulate additional
research in the budding area of natural language-guided programming.
