Navigating Application Modernization - Leveraging Gen-AI

Stop manually refactoring legacy Java code. See how combining static analysis with an LLM automatically transforms old EJBs into modern, verified, and container-ready applications.

#1about 3 minutes

The challenges of modernizing legacy applications

Technical debt, security vulnerabilities like Log4Shell, and high maintenance costs create significant challenges when updating older applications.

#2about 4 minutes

Analyzing application portfolios with the Konveyor project

The open source Konveyor project performs static code analysis to create an application inventory and generate detailed reports on migration risks and effort.

#3about 9 minutes

Demo: Migrating a JMS message driven bean to reactive

A live demonstration shows how Konveyor AI uses a large language model to automatically convert a legacy Java Message Service (JMS) bean to a modern reactive messaging implementation.

#4about 3 minutes

Demo: Converting a remote EJB into a modern REST API

The tool automatically transforms a remote Enterprise JavaBean (EJB) that uses the RMI-IIOP protocol into a standard, modern REST API endpoint.

#5about 6 minutes

How Konveyor AI uses RAG and agents for code generation

Konveyor AI leverages Retrieval-Augmented Generation (RAG) with static analysis data to provide context to any LLM, using agents to compile and validate the generated code.

#6about 1 minute

The end-to-end accelerated migration workflow

The developer workflow involves checking out code, configuring migration targets, running the analysis, and applying the AI-generated patch to complete the migration.