Syed M Shaaf - Application Modernization Leveraging Gen-AI for Automated Code Transformation

#1about 6 minutes

Understanding the business impact of technical debt

Technical debt increases costs, introduces security risks, and reduces business agility by hindering the adoption of new frameworks and cloud migration.

#2about 3 minutes

Identifying common vulnerabilities in legacy Java code

Legacy Java applications often contain outdated patterns like EJBs and critical vulnerabilities such as text4shell or log4shell that require identification and remediation.

#3about 8 minutes

Performing static code analysis with Konveyor.io

Konveyor's rules engine uses the Language Server Protocol (LSP) to perform static code analysis, identifying specific incidents and migration paths in a codebase.

#4about 6 minutes

How Konveyor AI automates code generation with LLMs

Konveyor AI combines static analysis with large language models using RAG and few-shot prompting to generate targeted code changes without requiring model fine-tuning.

#5about 11 minutes

Demo of automated code transformation with Kai

A live demonstration shows how Kai automatically transforms a Java EE application to Quarkus by converting JavaX to Jakarta, JMS to reactive messaging, and EJBs to REST endpoints.

#6about 2 minutes

Understanding the scope of automated modernization

The tool focuses on migrating technology stacks, like from a Java EE monolith to a Quarkus monolith, but does not perform higher-level re-architecture or domain-driven design.

#7about 3 minutes

Key differentiators of the Konveyor AI approach

This approach provides structured, auditable migrations driven by static analysis rules, is model-agnostic, and generates a reviewable code patch for developers.

#8about 1 minute