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

TL;DR

This paper presents WIRL, an LLM-based agent that automates context-aware code adaptation in IDEs by intelligently wiring unresolved variables, significantly improving accuracy over existing methods and IDE tools.

Contribution

WIRL introduces a novel retrieval-augmented generation approach for code wiring, combining rule-based and decision-guided strategies to enhance context-aware code adaptation.

Findings

Achieves 91.7% exact match precision and 90.0% recall.

Outperforms advanced LLMs by over 20 percentage points in precision.

Surpasses IntelliJ IDEA's performance by nearly 55 percentage points.

Abstract

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.