vFunction Introduces GenAI-Powered Architectural Intelligence for Code Assistants to Accelerate Application Modernization as Part of Cloud Migrations
New capabilities generate architecture-aware prompts that guide code assistants including Amazon Q Developer and GitHub Copilot in refactoring and cloud-native transformation, compressing modernization timelines from months to days
vFunction, the pioneer of AI-driven architectural observability and modernization, announced the industry’s first GenAI-powered capabilities to refactor and rearchitect applications. Unlike other code generation tools, vFunction leverages a unique combination of static and dynamic analysis with its own proprietary data science to efficiently remediate architectural technical debt, streamline refactoring, and enable service transformation for faster, more efficient modernization. This latest release brings vFunction’s architectural context to any GenAI assistant, including native integrations with Amazon Q Developer and GitHub Copilot to guide developers through automated architectural modernization and GenAI-powered service transformation. With these new capabilities, developers can seamlessly remove dependencies, increase modularity, convert outdated patterns, extract services, and support adding APIs for agentic integration and transformation. Engineers can achieve faster, more consistent architectural modernization directly from their integrated development environment (IDEs) and command line.
vFunction’s GenAI is enriched with deep architectural knowledge that is aware of semantic structures like context, components, and logical domains, enabling code assistants to address system-wide architectural challenges with complete architectural awareness, rather than just isolated code modifications. Unique to vFunction is its ability to generate insights and context based on the combination of static analysis, dynamic analysis, and proprietary data science that identifies optimal application domains—information that no other platform can provide. By bringing architectural intelligence into developers’ workflows, vFunction accelerates application modernization, helping organizations move beyond lift-and-shift to fully maximize their cloud investments.
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“With these new advancements, teams can surface and resolve architectural debt, and transform their apps to cloud-native, with unprecedented speed through autonomous modernization,” said Amir Rapson, CTO and co-founder of vFunction. “From eliminating circular dependencies to refactoring ‘god classes’, developers can now simplify refactoring and modernization, accelerate delivery, and optimize architecture for the cloud.”
First GenAI Platform for Automated Code Refactoring with Architectural Intelligence
According to the 2024 Stack Overflow Developer Survey, 76% of developers are using or plan to use AI tools in their development process. However, until now, GenAI in software development has been limited to generating new code snippets or automating repetitive tasks. vFunction shifts that paradigm by enabling GenAI to automatically refactor code with architectural awareness, solving structural issues with context-aware knowledge rather than just syntax suggestions. Developed by AI experts and trained on real customer applications, vFunction’s optimized prompts are enriched with architectural context to ensure consistent, predictable results, solving the common challenge of AI assistants producing varying outputs.
One of the ways vFunction is addressing GenAI-based refactoring is with its new MCP server, connecting vFunction’s architectural observability engine with modern developer environments. It enables developers to query architectural issues, generate GenAI prompts, and kick off remediation—all from the command line. With optimized support for Amazon Q Developer and GitHub Copilot, developers can use their preferred assistants to resolve architectural issues using prompts enriched with real-time architectural data. This closes the divide between architects and developers, making the architectural vision executable within native workflows.
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The various architectural-issues found by vFunction during its observation of the application now come with a GenAI method of addressing them, including: removing dead code and dependencies that are unneeded in certain contexts; refactoring out domain dependencies and calls between domains; refactoring design patterns of utility classes depending on core business logic; simplifying, splitting and re-architecting god classes; refactoring out circular flows between domains that affect resiliency and scalability; and splitting overgrown classes that are used differently by several domains.
“We’re turning architectural observability into action,” said Rapson. “Instead of just showing teams what’s wrong with their applications, we’re giving them AI-powered tools to fix these problems automatically. This bridges the gap between identifying architectural issues and resolving them to accelerate cloud modernization by orders of magnitude, taking the modernization process from months to days.”
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