# Natural Language-Guided Programming

arXiv: https://arxiv.org/abs/2108.05198 · HTML: https://mlsanity.com/paper/2108.05198/

## 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 (most cited first, 27 of 27)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [CodeSearchNet Challenge: Evaluating the State of Semantic Code Search](https://mlsanity.com/paper/1909.09436/) (2019)
- [IntelliCode Compose: Code Generation Using Transformer](https://mlsanity.com/paper/2005.08025/) (2020)
- [Learning to Mine Aligned Code and Natural Language Pairs from Stack Overflow](https://mlsanity.com/paper/1805.08949/) (2018)
- [Mapping Language to Code in Programmatic Context](https://mlsanity.com/paper/1808.09588/) (2018)
- [TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation](https://mlsanity.com/paper/1810.02720/) (2018)
- [PyMT5: multi-mode translation of natural language and Python code with transformers](https://mlsanity.com/paper/2010.03150/) (2020)
- [A parallel corpus of Python functions and documentation strings for automated code documentation and code generation](https://mlsanity.com/paper/1707.02275/) (2017)
- [JuICe: A Large Scale Distantly Supervised Dataset for Open Domain Context-based Code Generation](https://mlsanity.com/paper/1910.02216/) (2019)
- [In-IDE Code Generation from Natural Language: Promise and Challenges](https://mlsanity.com/paper/2101.11149/) (2021)
- [Program Synthesis using Natural Language](https://mlsanity.com/paper/1509.00413/) (2015)
- [Deep API Learning](https://mlsanity.com/paper/1605.08535/) (2016)
- [Incorporating External Knowledge through Pre-training for Natural Language to Code Generation](https://mlsanity.com/paper/2004.09015/) (2020)
- [StaQC: A Systematically Mined Question-Code Dataset from Stack Overflow](https://mlsanity.com/paper/1803.09371/) (2018)
- [Neural Code Search Revisited: Enhancing Code Snippet Retrieval through Natural Language Intent](https://mlsanity.com/paper/2008.12193/) (2020)
- [An Empirical Study of Obsolete Answers on Stack Overflow](https://mlsanity.com/paper/1903.12282/) (2019)
- [SWIM: Synthesizing What I Mean](https://mlsanity.com/paper/1511.08497/) (2015)
- [Toxic Code Snippets on Stack Overflow](https://mlsanity.com/paper/1806.07659/) (2018)
- [Reading StackOverflow Encourages Cheating: Adding Question Text Improves Extractive Code Generation](https://mlsanity.com/paper/2106.04447/) (2021)
- [From Query to Usable Code: An Analysis of Stack Overflow Code Snippets](https://mlsanity.com/paper/1605.04464/) (2016)
- [Maybe Deep Neural Networks are the Best Choice for Modeling Source Code](https://mlsanity.com/paper/1903.05734/) (2019)
- [Neural Sketch Learning for Conditional Program Generation](https://mlsanity.com/paper/1703.05698/) (2017)

## Built on by (most cited first, 4 of 4)

- [Natural Language to Code Generation in Interactive Data Science Notebooks](https://mlsanity.com/paper/2212.09248/) (2022)
- [Conversing with Copilot: Exploring Prompt Engineering for Solving CS1 Problems Using Natural Language](https://mlsanity.com/paper/2210.15157/) (2022)
- [Automating Code-Related Tasks Through Transformers: The Impact of Pre-training](https://mlsanity.com/paper/2302.04048/) (2023)
- [ObSynth: An Interactive Synthesis System for Generating Object Models from Natural Language Specifications](https://mlsanity.com/paper/2210.11468/) (2022)

## 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.