mlsanityData to 14 Oct 2026

Natural Language-Guided Programming

arXiv 2108.05198 ↗First posted 11 Aug 2021cs.SE cs.LG cs.PLcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 27 of its 56 references that are in the map. First posted on arXiv 11 Aug 2021.

Builds on

27 of 56 references.

References · 27

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15 more
  1. 2017A parallel corpus of Python functions and documentation strings for automated code documentation and code generationcited by 57
  2. 2019JuICe: A Large Scale Distantly Supervised Dataset for Open Domain Context-based Code Generationcited by 49
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Built on by

Cited by year

Citing papers per year: 2022: 3; 2023: 1.

4 papers.

  1. 2022Natural Language to Code Generation in Interactive Data Science Notebookscited by 87
  2. 2022Conversing with Copilot: Exploring Prompt Engineering for Solving CS1 Problems Using Natural Languagecited by 82
  3. 2023Automating Code-Related Tasks Through Transformers: The Impact of Pre-trainingcited by 8
  4. 2022ObSynth: An Interactive Synthesis System for Generating Object Models from Natural Language Specificationscited by 1