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World Congress 2024 • Aug 20, 2024 • Session details

Supercharging Static Code Analysis: Konveyor AI & LLMs

Daniel Oh

What if you could automate legacy migrations without hitting LLM token limits? Discover how Konveyor AI combines static analysis and RAG to safely refactor monolithic codebases.

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#1 about 3 min

Challenges in traditional application modernization

Traditional application modernization methods often trap developers in frustrating cycles of trial and error using basic search and generic AI tools.

#2 about 2 min

Key drivers and benefits of modernizing applications

Modernization improves codebase security, enhances cloud reliability, scales efficiently, and optimizes workloads for modern infrastructure.

#3 about 2 min

Defining application modernization through the Konveyor project

The Konveyor open-source CNCF project identifies modernization as upgrading CI/CD pipelines, restructuring monolithic data, and expanding service automation.

#4 about 3 min

Evaluating core strategies for application migration

Organizations can navigate cloud migration by choosing from six distinct strategies including rehosting, replatforming, refactoring, and repurchasing.

#5 about 2 min

Addressing refactoring challenges with AI tools

Developers can overcome complicated refactoring tasks by adopting API-driven development and implementing AI analysis modules.

#6 about 3 min

Applying customizable rules for static code analysis

The Konveyor project uses extensible rule sets to analyze artifacts and source code alignments against target cloud environments.

#7 about 3 min

Accelerating code generation with Konveyor AI

Konveyor AI connects to local or remote large language models to deliver precise code snippets and direct solutions for modernization errors.

#8 about 3 min

Setting up Konveyor AI and PostgreSQL locally

Running a local AI server alongside a PostgreSQL database virtual environment prepares the system to catalog code migration solutions.

#9 about 4 min

Configuring the Konveyor AI extension in Visual Studio Code

The IDE extension scans legacy Java applications and evaluates custom configuration rules to identify potential refactoring issues for target cloud runtimes.

#10 about 6 min

Enhancing code suggestions with retrieval-augmented generation

Konveyor AI leverages retrieval-augmented generation to bypass token limits and reuse previously validated fixes to augment new prompts.

#11 about 5 min

Reviewing and accepting AI recommendations in the IDE

Developers can easily inspect side-by-side file differences generated by the AI model to quickly update old namespaces and reactive messaging setups.

#12 about 3 min

Running the modernized Quarkus application with test containers

Deploying the refactored retail microservice showcases how the modernized environment seamlessly connects to a local containerized Postgres instance.

#13 about 2 min

Comparing Konveyor AI to automated coding assistants

Unlike standard coding copilots, Konveyor AI operates across entire organizational repositories to share custom refactoring configurations between developer teams.

Matching moments

2:02 min

Combining static analysis with generative AI

Shaaf Syed Shaaf Syed · WWC 2025

1:37 min

Open source static code analysis with Conveyor

Shaaf Syed Shaaf Syed · WWC 2025

1:27 min

Migrating legacy applications using generative AI

Shaaf Syed Shaaf Syed · WWC 2025

1:55 min

Shifting developer workloads and realistic AI productivity gains

Chris Heilmann +2 · LIVE

1:05 min

Modernizing legacy code repositories for robust artificial intelligence

April Yoho April Yoho · WWC Europe 2026

1:56 min

Analyzing cloud-based AI code completion architectures

Daniel Savenkov Daniel Savenkov · WWC 2024

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