AI Tools for Jakarta EE

Introduction

Artificial Intelligence (AI) is transforming the world of software development, including Jakarta EE. Traditionally, developers relied on project generators, visual designers, and IDE auto-completion to build applications. However, these tools often lacked adaptability, context awareness, and efficiency. AI-powered tools such as Payara Starter and Jeddict AI Assistant are now addressing these limitations, accelerating development workflows, and empowering developers to focus on innovation. This article explores how these tools enhance Jakarta EE development, using a conference application as a practical example. Additionally, it discusses future possibilities, including AI assistants integrated with Payara Server for server setup and diagnostics.

Traditional Development Challenges

Jakarta EE developers have long relied on conventional tools that, while helpful, come with limitations that can slow down development and increase the effort required for customization, testing, and documentation:

1. Project Generators and Archetypes: These tools help developers quickly scaffold applications using predefined templates. However, these templates are often rigid and require extensive customization to meet specific project requirements. This process can be time-consuming, especially for large-scale applications.
2. Visual Designers: Visual tools allow developers to create database schemas and design UI layouts. However, these tools lack contextual awareness, meaning every component placement must be done manually. This manual process can lead to inconsistencies and slower development times.
3. IDE Auto-Completion: IDEs provide auto-completion features that assist with basic syntax and code suggestions. However, these suggestions are limited because they do not fully understand the project’s context, which means they cannot provide complex or high-level recommendations.
4. Manual Testing and Documentation: Writing test cases and documentation by hand is time-consuming. Ensuring comprehensive test coverage and maintaining up-to-date documentation is essential for high-quality software, yet it often requires significant effort.

These limitations can slow down development cycles, increase the risk of errors, and require developers to spend more time on repetitive tasks instead of focusing on innovation and core functionality.

The AI Powered Tools for Jakarta EE Developer

Generative AI tools are transforming Jakarta EE development by addressing these challenges and offering smarter, context-aware solutions:

1. Adaptive Project Generators: Unlike traditional project generators, AI-powered generators can create application scaffolds based on natural language prompts. These generators are adaptive, meaning they automatically select appropriate icons, titles, descriptions, and dynamic page content based on the application’s context, reducing the need for manual adjustments.
2. Context-Aware Visual Designers: AI-driven visual designers can operate using natural language commands. This allows developers to describe the desired UI and database structure, and the AI auto-designs the application based on the understanding of existing context and best practices. This capability eliminates the need for manual component placement and ensures consistent design.
3. Natural Language Archetypes: Traditional archetypes are replaced with AI-driven code generation that responds to natural language commands. Developers can simply describe the functionality they need—such as “Create a REST endpoint for customer data”—and the AI generates the required code automatically, accelerating development.
4. Automated Test and Documentation Generation: AI can generate relevant test cases and comprehensive documentation by analyzing the code context. This automation ensures accuracy and completeness, saving developers significant time.
5. Intelligent Code Completion: AI-powered code completion goes beyond syntax suggestions by understanding the entire project’s context. Context-aware code completion results in smarter, more relevant code suggestions that help developers write cleaner, more efficient code.

By automating repetitive and time-consuming tasks, AI tools enable Jakarta EE developers to focus on building core functionality, enhancing user experiences, and driving innovation. This transformation leads to faster development cycles, higher-quality software, and greater overall productivity.

Let’s explore some powerful AI tools that are transforming the Jakarta EE development landscape:

Payara Starter

Payara Starter (https://start.payara.fish/) is a powerful AI-powered tool designed to simplify Jakarta EE application development. Using Entity-Relationship (ER) diagrams, developers can quickly design application architectures without manual setup. Generative AI in Payara Starter automatically creates ER diagrams based on natural language descriptions, allowing developers to refine diagrams using live chat commands.

Example: JCON EUROPE Conference Application

Payara Starter was used to generate an ER diagram for a conference application. By describing the application requirements (e.g., “JCON EUROPE Conference”), the tool created a diagram with entities such as Conference, Attendee, and Session, establishing relationships between them.

Using natural language commands, the diagram was expanded to include additional entities like Feedback and Registration, demonstrating the tool’s context awareness.

With the “+ enlarge” button, the diagram was further expanded to include entities like Sponsor, Material, and Reply. This feature allows developers to iteratively refine their application design, ensuring comprehensive coverage of all business requirements.

On further expanding the diagram with “+ enlarge” button, new entities like Networking Event and Workshop added to diagram.

Once the design was finalized and Jakarta EE, Payara, and JDK versions were selected, the application was generated with a single click.

The AI not only constructs the Jakarta EE backend structure with JPA entity classes and relationships but also auto-generates the frontend. User interfaces are designed with realistic titles, descriptions, and icons, tailored to each entity’s function. This automation significantly reduces frontend development time while maintaining consistent UI design.

This capability represents a hybrid approach, combining the contextual adaptability of LLMs with the structured templates of traditional archetypes. While the LLM understands natural language descriptions and generates dynamic content, archetypes ensure consistent code scaffolding and design patterns. Together, they streamline both database schema creation and frontend UI design, accelerating development without compromising flexibility or consistency.

This significantly reduced development time, enabling developers to focus on core functionality.

Jeddict AI Assistant

Jeddict AI Assistant (https://jeddict.github.io/) is an AI-powered tool available for Apache NetBeans IDE, designed to streamline Java and Jakarta EE development. It offers intelligent code suggestions, context-aware variable and method naming, automated test generation, and more. With support for multiple LLM providers, developers can customize configurations to fit their workflow, ensuring efficiency and consistency.

Key Features

• Intelligent Code Completion: Provides real-time, context-aware suggestions as developers type, reducing coding time and improving productivity.
• Inline Hints in Code Editor: Displays contextual hints directly in the editor, helping developers understand method usage, expected parameters, and best practices.
• Context-Aware Naming: Suggests meaningful variable and method names based on the surrounding code, aligning with standard naming conventions.
• Inline SQL Completion: Simplifies database interactions by offering real-time SQL suggestions as developers write queries.
• Automated Logging and Documentation: Generates clear, concise log messages and comprehensive Javadocs for classes and methods.
• Test Case Generation: Automatically creates relevant test cases based on the context of classes and methods, promoting best testing practices.
• Multiple LLM Provider Support: Allows developers to choose from different large language model providers with customizable configurations, including custom headers for secure API integrations.
• Special Support for Jakarta EE and Java EE Projects: Recognizes both javax and jakarta imports, ensuring compatibility with legacy and modern enterprise applications.
• REST Endpoint Generation: Quickly generates REST endpoints, including resource classes, annotations, and request/response models, reducing development time.
• Context-Aware AI Chat: Enables developers to select specific Java files or packages to provide context for AI-driven conversations, making it easier to get relevant suggestions and insights.
• AI-Generated Commit Messages: Automatically generates clear and meaningful commit messages based on code changes, ensuring better version control and collaboration.

Jeddict AI Assistant empowers developers to focus on building core functionality while automating repetitive tasks, enhancing productivity, and improving code quality across Java and Jakarta EE projects.

Conclusion

AI tools like Payara Starter and Jeddict AI Assistant are revolutionizing Jakarta EE development by automating repetitive tasks, accelerating coding, and enhancing code quality. With features like intelligent code completion, context-aware variable naming, automated test generation, and seamless REST endpoint creation, these tools empower developers to focus on building core functionality and delivering high-quality applications faster. Additionally, AI-generated documentation and commit messages streamline collaboration, ensuring teams can maintain consistency and share knowledge effectively.

Looking ahead, the potential for an AI Assistant specifically designed for Payara Server could further enhance productivity for developers of all skill levels. Beginners could benefit from simplified server setup and step-by-step guidance without needing advanced knowledge of asadmin commands. At the same time, experienced users could leverage AI for advanced diagnostics, log analysis, and performance optimization, ensuring efficient and reliable server operations. Automated server configuration through natural language commands would further streamline deployment and management, reducing the complexity of server administration.

As AI continues to evolve, its integration into Jakarta EE development promises smarter, faster, and more efficient workflows. By reducing manual effort and automating time-consuming tasks, AI empowers developers to focus on innovation, driving the creation of robust, scalable, and high-performing applications. With tools like Payara Starter, Jeddict AI Assistant, and future advancements in AI-driven server management, the future of Jakarta EE development is poised to be more productive and accessible than ever before.