# Context Composing for Full Line Code Completion

arXiv: https://arxiv.org/abs/2402.09230 · HTML: https://mlsanity.com/paper/2402.09230/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 7 references that are in the map.
- First posted on arXiv 14 Feb 2024.

## Builds on (most cited first, 2 of 2)

- [Improving language models by retrieving from trillions of tokens](https://mlsanity.com/paper/2112.04426/) (2021)
- [InCoder: A Generative Model for Code Infilling and Synthesis](https://mlsanity.com/paper/2204.05999/) (2022)

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

- [Statically Contextualizing Large Language Models with Typed Holes](https://mlsanity.com/paper/2409.00921/) (2024)
- [Mellum: Production-Grade in-IDE Contextual Code Completion with Multi-File Project Understanding](https://mlsanity.com/paper/2510.05788/) (2025)
- [Retrieval-augmented code completion for local projects using large language models](https://mlsanity.com/paper/2408.05026/) (2024)
- [Relative Positioning Based Code Chunking Method For Rich Context Retrieval In Repository Level Code Completion Task With Code Language Model](https://mlsanity.com/paper/2510.08610/) (2025)

## Abstract (arXiv)

Code Completion is one of the most used Integrated Development Environment (IDE) features, which affects the everyday life of a software developer. Modern code completion approaches moved from the composition of several static analysis-based contributors to pipelines that involve neural networks. This change allows the proposal of longer code suggestions while maintaining the relatively short time spent on generation itself. At JetBrains, we put a lot of effort into perfecting the code completion workflow so it can be both helpful and non-distracting for a programmer. We managed to ship the Full Line Code Completion feature to PyCharm Pro IDE and proved its usefulness in A/B testing on hundreds of real Python users. The paper describes our approach to context composing for the Transformer model that is a core of the feature's implementation. In addition to that, we share our next steps to improve the feature and emphasize the importance of several research aspects in the area.