Blog
March 5, 2026
Java developers have always adapted to new tooling: from the first IDEs, to build automation, testing frameworks, and beyond. Now, AI is making its way into every part of the Java development workflow, and the options are multiplying fast.
The question is no longer whether to adopt AI in your Java development practice, but which approach makes the most sense for your business and how you work. This guide covers how AI writes Java code, where it adds the most value, and how today's AI-powered tools — from traditional IDE plugins to fully AI-native IDEs — compare.
Read on to learn:
Table of Contents
- Why Java Developers Should Use AI Tools
- How Does Generative AI Write Java Code?
- Use Cases for AI in Java Development
- AI-Native IDEs vs. AI Plugins for Traditional Java IDEs
- How AI-Native IDEs Change Development Workflow
- AI Plugins for IntelliJ IDEA, VS Code, and Eclipse
- Comparing AI-Native IDEs: Cursor, Windsurf, Kiro, and Antigravity
- Final Thoughts
Why Java Developers Should Use AI Tools
Writing an application in Java involves a lot of repetitive work. Boilerplate code, getter/setter generation, null-checks, and unit test scaffolding are all tasks most developers can complete with their eyes closed — but they still take time away from other tasks.
Direct Time to Higher-Value Work
Generative AI tools address this time sink directly. By automating the rote parts of coding, developers can redirect energy toward higher-value work: improving application architecture, solving complex bugs, and building features that matter to their business. When AI handles routine code generation, developers stay focused on more complex tasks.
Improve Java Code Quality
Beyond speed, AI tools also improve code quality. They surface potential bugs early, suggest more efficient patterns, and catch issues that might otherwise slip through code review. For Java teams working across microservices and remote development environments, that kind of assistance compounds across every sprint.
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What Is Vibe Coding?
Back to topVibe coding is when a Java developer writes an AI prompt in plain language and the generative AI tool returns working Java code.
How Does Generative AI Write Java Code?
Generative AI tools write Java code by interpreting natural language prompts through large language models (LLMs). These models are trained on large datasets of existing code, which means they recognize common Java idioms and standard design patterns. The output reflects those learned patterns.
That said, AI-generated code is not infallible. Because the models learn from existing code samples, they can inadvertently replicate errors, outdated practices, or suboptimal patterns. Developers should always review, test and validate code developed through vibe coding.
Use Cases for AI in Java Development
AI might not be able to write an entire Java application from start to finish (yet), but it can help automate some of the more tedious processes or augment tasks that are prone to error. Learn more about some of the up-and-coming use cases for AI in Java development in this video clip with Perforce CTO Rod Cope.
Intelligent Code Completion
Intelligent code completion tools analyze the full context of your code — the class structure, method signatures, variable names, and surrounding logic—and predict what you are likely to write next. This includes completing entire statements, suggesting method implementations, and offering contextually relevant snippets from across the codebase.
The practical result: fewer keystrokes, less context-switching, and faster iteration through repetitive code patterns.
🧰 Further Reading: Learn more about prompt engineering for AI
Error Detection and Bug Fixing
Traditional static code analyzers, like Klocwork, identify many common issues in their application, but AI-enhanced tools can detect more complex problems by learning from a vast dataset of coding errors and fixes. By learning from a vast dataset of coding errors and their corresponding fixes, AI-powered tools can detect more nuanced bugs such as logic errors, concurrency issues, and edge cases that traditional analyzers often miss.
Some tools go further, not only flagging issues but applying fixes automatically, reducing the time developers spend diagnosing and resolving problems.
Refactoring and Code Optimization
AI tools can analyze existing code for inefficiencies, redundancies, and violations of best practices to suggest improvements. This is particularly valuable for legacy Java codebases, where technical debt accumulates over time.
Rather than manually reviewing every method for optimization opportunities, developers can use AI to surface the highest-impact refactoring targets and apply changes with confidence.
Unit Test Generation
AI agents can suggest test cases based on historical data, coding patterns, and best practices, saving developers significant time and effort. This not only accelerates the development cycle but also allows Java developers to focus on writing feature-rich code instead of unit test generation.
Code Review
AI agents can help reduce the time required for manual reviews by detecting patterns and suggesting improvements (e.g., optimizing algorithms, refactoring redundant code, or enhancing readability).
AI-Native IDEs vs. AI Plugins for Traditional Java IDEs
This is where the Java development landscape splits into two distinct approaches for using AI tools to write Java code.
AI plugins for traditional IDEs add AI capabilities on top of an existing environment. Developers keep the IDE they know and love (e.g., IntelliJ IDEA, VS Code, or Eclipse) and install a plugin that provides features like code completion, chat-based assistance, and error detection. The IDE itself remains the primary interface; the AI is an assistant within it.
AI-native IDEs, on the other hand, are built from the ground up with AI at the center. The AI doesn’t just respond to prompts — it actively plans, writes, refines, and tests code as an autonomous collaborator. Developers describe goals in plain language, and the agent takes multi-step action to achieve them. AI-native IDEs manage context, tracks changes, and handles tasks with a level of autonomy that AI plugins for traditional IDEs like IntelliJ, Eclipse, and VS Code cannot match.
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Both approaches have their place. Plugins offer lower friction that help teams maximize their investment in an existing IDE (e.g., IntelliJ). AI-native IDEs offer greater capability for multifunctional coding assistance that’s directly integrated within development workflows.
| Feature | Traditional IDE + AI Plugin | AI-Native IDE |
| Architecture | AI works as an assistant. Restricted by IDEs API Limits. | AI works as a collaborator and has access to the terminal, file system, and browser. |
| Workflow | AI provides suggestion per the developer’s prompt. | A developer gives a task (e.g., "Add OAuth"), and the IDE executes it. |
| Interaction | Prompt -> suggestion | Task -> plan -> execute -> observe |
| Context | Short context that focuses on opened/attached file and dependencies. | The AI indexes the entire repo, docs, etc. |
| Verification | A developer must run the code to see if the AI-generated code worked. | AI agents can run the server, check logs, and fix their own bugs. |
| Core Difference | AI plugins for traditional IDEs help developers write code. | AI-native IDEs attempt to complete engineering tasks. |
How AI-Native IDEs Change Development Workflow
In a traditional Java development loop, a developer writes code, waits for a build, runs tests, and then fixes errors. Even when using JRebel to eliminate redeploys and XRebel to spot performance issues faster, that process still takes time. In an AI-native IDE, that entire development workflow is automated with minimal developer involvement required.
Traditional Java Development Loop
AI-Native IDE Development Loop
AI Plugins for IntelliJ IDEA, VS Code, and Eclipse
There are some key advantages in using an IDE-based AI code generator — the most important being that the Java developer doesn’t need to break their flow, leave the IDE, or rely on copious amounts of copy/paste. This can reduce the opportunity for errors while increasing productivity.
Learn more about some of the AI plugins available today.
JetBrains AI in IntelliJ IDEA
IntelliJ IDEA remains the most widely used Java IDE, and JetBrains has built AI directly into the platform. JetBrains AI Assistant provides unlimited code completion, a chat interface for asking questions about the codebase, and access to multiple LLMs — including models from OpenAI, Anthropic, and Google, as well as JetBrains' own Mellum model, which is optimized specifically for code completion.
Because the AI agent is embedded natively in the IDE, it has full context awareness. Developers can ask short, direct questions rather than crafting detailed external prompts. JetBrains also offers Junie, an agentic coding assistant that can plan, write, and test code directly within IntelliJ IDEA, further bridging the gap between a plugin and an AI-native IDE.
GitHub CoPilot in Microsoft VS Code
GitHub Copilot is one of the most widely adopted AI coding assistants and integrates cleanly into VS Code via extension. It provides inline code suggestions, natural language chat, terminal assistance, and the ability to ask questions about unfamiliar codebases. Copilot is language-agnostic, which makes it a practical choice for developers working in polyglot development environments.
GitHub Copilot in VSCode offers several features, including:
- Offering developers to get inline code suggestions while they’re writing and iterating code
- Fixing errors in code
- Resolving errors while running commands in the terminal
- Asking questions to help ramp-up on a new code base
- Using chat features to configure your VS Code setup
📚 Further Reading: All you need to know about the VS Code extension for JRebel
AI in Eclipse
Eclipse has integrated AI capabilities to enhance its error detection features. This feature is used by analyzing code patterns and comparing them against known bug patterns, allowing Eclipse developers more precise and actionable insights. plugins that lend AI functionality, including Tabnine, aiXcoder, and Remain Ai Chat.
Back to topComparing AI-Native IDEs: Cursor, Windsurf, Kiro, and Antigravity
AI-native IDEs are built with AI embedded as the core component, but each functions a little bit differently. Learn more about the differentiators between Cursor, Windsurf, Google Antigravity, and AWS Kiro.
Cursor
Cursor is an AI-native IDE built on a VS Code fork; it has gained significant traction among developers who want deep AI integration. Cursor’s composer agent handles multi-step tasks autonomously: planning implementations, writing code across multiple files, running tests, and iterating based on results. Cursor also includes a specialized tab model for highly accurate autocomplete and integrates with external services like GitHub and Slack.
Windsurf
Windsurf, developed by Codeium, markets itself as the most intuitive AI coding experience. Its Cascade agent combines codebase understanding, real-time awareness of developer actions, and a broad set of tools to maintain a "flow state" during development. Windsurf also supports MCP (Model Context Protocol) for connecting external tools and services, automatic lint fixing, and drag-and-drop image input to generate UI code.
Kiro
AWS has entered into the AI-native IDE space with Kiro. It is designed to work closely with AWS services and is aimed at developers building cloud-native applications. Kiro focuses on spec-driven development — developers define requirements, and the agent generates code aligned to those specs — making it particularly well-suited for teams with highly structured development processes.
Antigravity
Antigravity is an emerging AI-native IDE focused on autonomous code generation and long-horizon task completion. It is designed for developers who want to delegate entire features or workflows to an AI agent, with the IDE handling planning, implementation, and testing as a unified process.
Back to topFinal Thoughts
There are advantages to both AI plugins for IntelliJ, VS Code, and Eclipse, and AI-native IDEs like Cursor, Windsurf, Kiro, and AntiGravity — it all comes down to your organization’s Java development needs. The good news is that JRebel can eliminate Java redeploys in all popular Java IDEs.
JRebel allows you to visualize code changes instantly while maintaining application state. That means the productivity gains from AI code generation don’t stall at the redeploy step. By using JRebel, developers iterate faster, test more frequently, and stay in flow.
While AI tools reduce the time it takes to write Java code, JRebel reduces the time it takes to see the results.
Compound Your Time Savings With JRebel
AI and JRebel address different parts of the development cycle, and together, they close the loop on two of the most time-consuming parts of Java development: writing Java code and seeing it run in your business application.
Start your 14-day free trial of JRebel today and compound the time savings already built into your AI-powered Java workflow.