# SmartPaste: Learning to Adapt Source Code

arXiv: https://arxiv.org/abs/1705.07867 · HTML: https://mlsanity.com/paper/1705.07867/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2017.

## Built on by (most cited first, 8 of 8)

- [What is it like to program with artificial intelligence?](https://mlsanity.com/paper/2208.06213/) (2022)
- [Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit](https://mlsanity.com/paper/2401.00288/) (2023)
- [AdaptivePaste: Code Adaptation through Learning Semantics-aware Variable Usage Representations](https://mlsanity.com/paper/2205.11023/) (2022)
- [A Survey on Natural Language Processing for Programming](https://mlsanity.com/paper/2212.05773/) (2022)
- [Assessing the Effectiveness of Syntactic Structure to Learn Code Edit Representations](https://mlsanity.com/paper/2106.06110/) (2021)
- [Wired for Reuse: Automating Context-Aware Code Adaptation in IDEs via LLM-Based Agent](https://mlsanity.com/paper/2507.01315/) (2025)
- [Smart Paste: Automatically Fixing Copy/Paste for Google Developers](https://mlsanity.com/paper/2510.03843/) (2025)
- [Coding in a Bubble? Evaluating LLMs in Resolving Context Adaptation Bugs During Code Adaptation](https://mlsanity.com/paper/2601.06497/) (2026)

## Abstract (arXiv)

Deep Neural Networks have been shown to succeed at a range of natural language tasks such as machine translation and text summarization. While tasks on source code (ie, formal languages) have been considered recently, most work in this area does not attempt to capitalize on the unique opportunities offered by its known syntax and structure. In this work, we introduce SmartPaste, a first task that requires to use such information. The task is a variant of the program repair problem that requires to adapt a given (pasted) snippet of code to surrounding, existing source code. As first solutions, we design a set of deep neural models that learn to represent the context of each variable location and variable usage in a data flow-sensitive way. Our evaluation suggests that our models can learn to solve the SmartPaste task in many cases, achieving 58.6% accuracy, while learning meaningful representation of variable usages.