# Wired for Reuse: Automating Context-Aware Code Adaptation in IDEs via LLM-Based Agent

arXiv: https://arxiv.org/abs/2507.01315 · HTML: https://mlsanity.com/paper/2507.01315/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 25 of its 55 references that are in the map.
- First posted on arXiv 2 Jul 2025.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 25 of 25)

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- [From LLMs to LLM-based Agents for Software Engineering: A Survey of Current, Challenges and Future](https://mlsanity.com/paper/2408.02479/) (2024)
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- [Large Language Model Inference Acceleration: A Comprehensive Hardware Perspective](https://mlsanity.com/paper/2410.04466/) (2024)
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- [CCT5: A Code-Change-Oriented Pre-Trained Model](https://mlsanity.com/paper/2305.10785/) (2023)
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- [SOTorrent: Reconstructing and Analyzing the Evolution of Stack Overflow Posts](https://mlsanity.com/paper/1803.07311/) (2018)
- [Deep Learning-based Software Engineering: Progress, Challenges, and Opportunities](https://mlsanity.com/paper/2410.13110/) (2024)
- [SmartPaste: Learning to Adapt Source Code](https://mlsanity.com/paper/1705.07867/) (2017)
- [Analyzing and Supporting Adaptation of Online Code Examples](https://mlsanity.com/paper/1905.12111/) (2019)
- [Overwatch: Learning Patterns in Code Edit Sequences](https://mlsanity.com/paper/2207.12456/) (2022)
- [APIzation: Generating Reusable APIs from StackOverflow Code Snippets](https://mlsanity.com/paper/2109.01138/) (2021)
- [CoEdPilot: Recommending Code Edits with Learned Prior Edit Relevance, Project-wise Awareness, and Interactive Nature](https://mlsanity.com/paper/2408.01733/) (2024)
- [Instruct or Interact? Exploring and Eliciting LLMs' Capability in Code Snippet Adaptation Through Prompt Engineering](https://mlsanity.com/paper/2411.15501/) (2024)
- [Optimising the Fit of Stack Overflow Code Snippets into Existing Code](https://mlsanity.com/paper/2004.07663/) (2020)
- [How Do Java Developers Reuse StackOverflow Answers in Their GitHub Projects?](https://mlsanity.com/paper/2308.09573/) (2023)
- [Recommending Variable Names for Extract Local Variable Refactorings](https://mlsanity.com/paper/2507.00413/) (2025)

## Abstract (arXiv)

Copy-paste-modify is a widespread and pragmatic practice in software development, where developers adapt reused code snippets, sourced from platforms such as Stack Overflow, GitHub, or LLM outputs, into their local codebase. A critical yet underexplored aspect of this adaptation is code wiring: the context-aware process of substituting unresolved variables in pasted code with suitable variables or expressions from the surrounding context. Existing solutions either rely on heuristic rules or historical templates, often failing to effectively utilize contextual information, despite studies showing that over half of adaptation cases are context-dependent. In this paper, we introduce WIRL, an LLM-based agent for code wiring framed as a Retrieval-Augmented Generation (RAG) infilling task. WIRL combines an LLM, a customized toolkit, and an orchestration module to identify unresolved variables, retrieve context, and perform context-aware substitutions. To balance efficiency and autonomy, the agent adopts a mixed strategy: deterministic rule-based steps for common patterns, and a state-machine-guided decision process for intelligent exploration. We evaluate WIRL on a carefully curated, high-quality dataset consisting of real-world code adaptation scenarios. Our approach achieves an exact match precision of 91.7% and a recall of 90.0%, outperforming advanced LLMs by 22.6 and 13.7 percentage points in precision and recall, respectively, and surpassing IntelliJ IDEA by 54.3 and 49.9 percentage points. These results underscore its practical utility, particularly in contexts with complex variable dependencies or multiple unresolved variables. We believe WIRL paves the way for more intelligent and context-aware developer assistance in modern IDEs.