JADE: A VSCode Plugin for Static Analysis-Guided AI-Assisted Refactoring

Abstract

Software developers often use static analysis tools to identify code warnings related to code quality in general. However, despite the availability of automated warnings, developers still need to manually interpret and apply the suggested fixes. Recent advances in Large Language Models (LLMs) have created new opportunities to support automated refactoring suggestions directly within development environments. This paper presents JADE (Java Static Analysis Repair), a Visual Studio Code plugin that combines static analysis knowledge with LLM-based refactoring suggestions. JADE adopts a Retrieval-Augmented Generation (RAG) strategy based on SonarQube rules, retrieving semantically relevant static analysis heuristics to enrich structured prompts submitted to local LLMs. The plugin supports AI-assisted code diagnostics and refactoring generation integrated into the VSCode workflow. In addition, JADE incorporates a developer feedback mechanism that allows users to evaluate the usefulness and relevance of the generated recommendations. An exploratory study involving 50 Java code snippets suggests that JADE complements traditional static analysis by identifying additional semantic refactoring opportunities while preserving the strengths of rule-based analysis for detecting explicit code quality issues.