Top AI Tools for Java and How They’re Transforming Development

 

As artificial intelligence (AI) continues to shape modern software development, Java developers are discovering a wealth of tools that improve everything from code generation to automated testing. Whether you're a backend engineer building APIs or maintaining large-scale enterprise systems, integrating AI tools for Java into your workflow can dramatically boost productivity and code quality.

In this article, we explore the top AI-powered tools and libraries designed to support Java development, highlight testing solutions like Keploy, and even share a bonus section for frontend developers working with React in Visual Studio Code.

Why Java Developers Need AI Tools

Java remains one of the most dominant programming languages, especially in enterprise, Android, and backend services. But with increasing complexity in modern systems, developers are often burdened with repetitive tasks like writing boilerplate code, debugging, and testing.

This is where AI tools step in — automating routine tasks, improving accuracy, enhancing code quality, and freeing developers to focus on solving real business problems.

1. Smart Code Generation and Autocompletion

Writing boilerplate code or remembering exact syntax is no longer necessary with AI-assisted code generators. These tools help you write better Java code faster with real-time suggestions and context-aware completions.

🔧 Tools You Can Use:

  • GitHub Copilot – Suggests full lines or entire methods based on comments and previous code.
  • Tabnine – Uses generative AI to deliver faster, more relevant code completions.
  • IntelliCode for IntelliJ – Learns from your Java codebase to offer smarter recommendations inside IntelliJ IDEA.

2. AI-Powered Testing Tools

Testing is one of the most time-consuming yet critical tasks in software development. AI can accelerate this by generating high-quality test cases, mocks, and helping developers increase coverage — especially for integration and unit tests.

🚀 Must-Try Tools:

  • Keploy – An open-source AI-powered testing tool that auto-generates test cases and mocks from real user traffic. Ideal for Java microservices and API development.
  • Diffblue Cover – Uses AI to automatically create unit tests for Java code, helping teams maintain better test coverage.
  • DeepCode (now part of Snyk) – Applies AI to detect bugs and security vulnerabilities during development.

3. Static Analysis and Code Review Using AI

AI can act like an intelligent reviewer that never sleeps. These tools help you catch potential bugs and enforce best practices even before human reviews.

🛡️ Tools to Consider:

  • Amazon CodeGuru – Offers intelligent suggestions for performance improvements and bug fixes in Java code.
  • SonarLint – Adds real-time analysis to your IDE with AI-tuned rule suggestions.
  • Codacy – Combines static analysis with machine learning to improve code quality over time.

4. AI Tools for Dependency and Build Management

Modern Java projects often involve complex build pipelines and dependency graphs. AI-driven tools simplify and automate much of this workflow.

⚙️ Noteworthy Tools:

  • Snyk – Scans Java dependencies for security issues and recommends fixes.
  • Renovate – AI-enabled bot that helps keep your dependencies up to date automatically.

These tools prevent vulnerabilities and reduce technical debt in long-running projects.

5. AI for Auto-Documentation and Knowledge Sharing

Understanding legacy Java code or onboarding a new developer can be challenging. AI tools now generate helpful summaries and documentation from your codebase.

📚 Try These:

  • OpenAI Codex – Can generate Javadoc-style comments and explain what complex methods do.
  • Sourcegraph Cody – Helps developers explore and understand unfamiliar Java codebases through AI-driven navigation.

6. AI Libraries and Frameworks for Java Developers

Want to build your own AI-powered application in Java? You're in luck — Java has several mature libraries to support deep learning, neural networks, and machine learning.

📦 Best Options:

  • Deep Java Library (DJL) – Developed by AWS, it supports TensorFlow, PyTorch, and MXNet.
  • DL4J (Deeplearning4j) – A production-grade, distributed deep learning library built for the JVM.
  • Neuroph – Lightweight and beginner-friendly framework for creating neural networks in Java.

📘 Dive deeper: Top AI Libraries and Tools for Java Developers

7. Best Practices for Using AI in Java Projects

To get the most out of AI tools, Java teams should:

  • Review AI-generated code manually to ensure correctness and security.
  • Balance automation with human oversight to avoid introducing bugs or overfitting solutions.
  • Integrate AI tools into CI/CD pipelines for automated code checks and test generation.

🧪 Related: Why AI-Generated Tests Are the Future of Automation

Bonus: How to Code React in Visual Studio Code

While the core of this article focuses on Java, many developers work in full-stack environments and also use React for the frontend.

If you're looking to code React in Visual Studio, you're in good hands. Visual Studio Code (VS Code) is a top choice for React developers thanks to its lightweight nature, smart IntelliSense, and powerful extensions.

You can also write and run React code online using cloud IDEs like StackBlitz or GitHub Codespaces — making experimentation and collaboration easier than ever.

🎯 Learn how: Running React Code in Visual Studio Code and Online

Conclusion

Integrating AI tools for Java into your development environment isn’t just a nice-to-have — it’s quickly becoming a necessity. These tools streamline your workflow, improve test coverage, enhance security, and ultimately lead to faster, more efficient development.

Whether you're using tools like Keploy to auto-generate tests or leveraging GitHub Copilot to write code faster, AI is here to enhance your Java development experience.


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