Natural Language-Guided Programming

TL;DR

This paper proposes a novel approach to code autocompletion that incorporates natural language descriptions of developer intent, demonstrated through a Python data science tool using language models trained on code corpora.

Contribution

It introduces the concept of natural language-guided programming and develops a tool that integrates natural language understanding into code autocompletion for Python data science tasks.

Findings

Feasibility demonstrated with a Python data science tool

Language models can incorporate natural language intent into code completion

Initial experiments show promising results but highlight the need for further research

Abstract

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.