This upcoming panel gathers together experts in the Java ecosystem who will share their perspectives on how AI is transforming enterprise-level server-side development. The panel highlighted advances in agentic IDEs, the rise of spec-driven methodologies, and how backend development roles are evolving.
The Future of Java is Becoming Smarter
In the ever-evolving world of Java, AI is no longer a distant promise — it’s a reality reshaping how we design, write, test, and maintain code. From intelligent autocomplete in your IDE to autonomous agents refactoring entire classes and packages, the Java developer’s toolbox is being re-imagined. Join a panel of industry leaders for a dynamic conversation about how generative AI is revolutionizing Java development workflows — from enterprise back-end systems to cloud-native microservices and beyond.
The XtremeJ 2025 Online Conference
The XtremeJ 2025 online conference returns for its 5th edition this December. This year’s highlight: a professional panel dedicated to exploring the impact of generative AI on software development in the Java ecosystem. You’ll gain practical insights, participate in live interaction, and leave with a forward-looking mindset.
Today’s Uses of Generative AI in the Java Ecosystem
Generative AI already plays a role across the Java ecosystem, and mainly on the server side. Whether using Java, Kotlin, or Scala, AI assists.
Code Completion & Assistance
Tools like GitHub Copilot, Tabnine, and JetBrains’ AI Assistant now help Java developers rapidly sketch out logic in Spring, Jakarta EE, Micronaut, Quarkus, or plain Java. They generate methods, suggest APIs, and help navigate complex object models and design patterns.
Project Scaffolding & Generation
AI can bootstrap entire Spring Boot or Micronaut applications — including controllers, services, repositories, tests, configuration files, and even Docker/Kubernetes manifests. Full architectural templates (REST, MVC, CQRS, event-driven) can be generated in minutes.
Code Review & Refactoring
Platforms such as SonarQube with GenAI, Amazon CodeGuru, and AI-enhanced IDEs now detect anti-patterns, concurrency risks, memory leaks, thread-safety issues, unused code, and performance bottlenecks across large Java codebases.
Test Generation & Coverage
AI accelerates the creation of JUnit 5 unit tests, integration tests, mocking layers (Mockito, MockK), and even system-level scenarios for Spring Boot test slices. It can produce parameterized tests, boundary checks, and test coverage recommendations.
Documentation & Code Explanation
From understanding complex Spring dependency injection flows to explaining legacy enterprise Java systems, AI can produce Javadoc, README files, architectural diagrams, sequence diagrams, and detailed explanations of multi-module applications.
Agentic Development
Emerging agentic IDEs and workflow tools (like Amazon’s Kiro, JetBrains AI Agents, and custom AI pipelines) allow autonomous agents to interpret requirements, modify code, implement features, generate tests, track issues, and integrate directly with CI/CD pipelines.
With Java’s ubiquity across enterprise systems, cloud-native microservices, Android, big data, and backend services, and with its mature ecosystem of frameworks and tooling, the language is becoming a powerful proving ground for AI-driven software development.
Questions for the Panel
What will the next era of Java server-side development look like? Here are some of the questions from which we will pick the ones we will explore:
Developer Skills & Roles
What core skills will Java developers need in an era dominated by spec-driven and AI-augmented development? How will AI reshape the role of Java developers—will coding become orchestration? Will system thinking outweigh coding? What does collaboration look like when developers, PMs, designers, and AI agents all contribute to specs?
Documentation & System Design
How should teams document their systems to harness AI-powered development fully? Are formal specs (EARS, UML, OAS) becoming essential?
Productivity, Technical Debt & Code Quality
Can agentic IDEs and autonomous tools help manage technical debt in a large-scale Java code base? What are the risks of over-relying on AI-generated code in mission-critical systems (auditability, consistency, trust)? How will AI impact performance optimization in modern web apps? Can it manage profiling, refactoring, and rewrite cycles automatically?
Testing, QA & Reliability
Will AI transform the way we test applications? Can AI generate and maintain test suites, detect flaky tests, and improve coverage autonomously? How do teams validate and verify changes produced by autonomous or semi-autonomous agents?
The panel will provide us all with practical, firsthand perspectives from professionals operating at the intersection of AI and software development.
We invite the Java community to contribute. If you have a question you’d like our Xperts Panel to tackle, please don’t hesitate to let us know.
