I always enjoy participating in KubeCon. This time it was at the RAI center in Amsterdam. I have been to many conferences and the ones that are the best IMHO are the ones that are very community focused. For example DevNexus for Java, GeeCon for Geeks ;), and obviously KubeCon for everything Kubernetes. And obvsiouly making new friends and connections is a great way of learning from all the cool stuff thats going on. Thats probably enough name dropping for a wednesday ;)
I had the opportunity to represent the Konveyor Community project update.
Modernizing Legacy Code with Konveyor AI: From EJB to Kubernetes
"We want to have a meaningful result out of generative AI, not just chatting around... we can context engineer that code, send it into the LLM, and get results integrated back into the codebase." — Shaaf Syed
Application modernization is a daunting task, especially when dealing with legacy codebases that are decades old. In my recent CNCF Project Lightning Talk, I introduced how the Konveyor community is leveraging AI and static analysis to automate this process for almost any programming language. While there are multiple ways of solving the migration and modernization challenges, Konveyor community takes a unique approach to it.
For example many enterprises still rely on 30-year-old Enterprise Java Beans (EJB) or legacy protocols like RMI/IIOP. When leadership asks to move these applications to Kubernetes, developers face a nightmare of serialization issues, stubs, and complex clustering logic that simply doesn't fit a modern cloud-native environment. It like you just encountered the "Legacy Wall!". Nothing can get passed that point. True, but hardly anymore IMHO. Why not use the traditional static code anaylsis to enahnce the responses of an LLM.
During the talk, I demonstrated a live example of an outdated EJB, i.e. being transformed into a modern REST service. What would normally take hours of manual refactoring was reduced to a generated Git patch that the developer could simply review and apply.
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Konveyor solves this by combining deep static code analysis with the power of Large Language Models (LLMs). The core workflow involves:
- Static Code Analysis: The engine analyzes source code (Java, Go, Python, NodeJS, etc.) to identify "incidents"—specific lines of code that won't run on Kubernetes.
- Context Engineering: By understanding the code paths via the Language Server Protocol (LSP), Konveyor provides the necessary context to an LLM.
- Automated Remediation: Instead of just chatting, the AI generates meaningful code fixes, such as replacing legacy protocols with REST endpoints or suggesting the use of Kubernetes Secrets and ConfigMaps.
Beyond Chat: Agents and Memory
What makes Konveyor AI particularly powerful is its move toward Agentic AI and Distributed Memory:
1. Agentic Validation
When AI fixes one part of the code, it often breaks another. Konveyor’s agents handle:
- Compilation: Ensuring the new code actually builds.
- Validation Testing: Running tests to verify functionality.
- Sanitization: Cleaning up the output before it reaches the developer.
2. Organizational Memory
If a developer modifies an AI-generated fix (e.g., changing how exceptions are handled), Konveyor can "remember" that preference. This memory is shared across the organization, so future migrations automatically follow the team's specific coding standards.
Get Involved
Konveyor is an open-source community project. You can find them at their CNCF kiosk or join the community to help build the future of automated application modernization.
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