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Legacy Java applications often contain outdated patterns like EJBs and critical vulnerabilities such as text4shell or log4shell that require identification and remediation.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
How to contribute to the Konveyor open source community
Developers interested in contributing to the project can join the Konveyor.io community through Slack, mailing lists, and regular community meetings.
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