AppFactor Introduces GenAI Driven Application Refactoring and Opens Early Access Program
AppFactor, the AI-augmented developer platform today announced an early access program for a new GenAI driven refactoring release that enables developers to automate the entire process of improving, evolving and maintaining their applications at scale, leveraging innovative GenAI enabled workflows.
“Our new GenAI-augmented solution solves these problems at a scale unlike anything else in the market.”
Over the past two years, models from the likes of ChatGPT and Anthropic Claude Sonnet have shown incredible innovation. Alongside those developments, the velocity of generative code solutions has ramped up, allowing AI to support developer use cases, from acting as an assistant, to generating deployment templates and enabling new, high-value use cases for software teams.
“Despite these innovations, conducting tasks like framework migrations or code refactoring across large/complex codebases sits at the edge of what is possible with current methods,” said Keith Neilson, CEO of AppFactor. “Our new GenAI-augmented solution solves these problems at a scale unlike anything else in the market.”
AppFactor’s new release is a state of the art, model-agnostic architecture providing:
- Static and dynamic code analysis with optimized code retrieval
- Agentic workflow architecture that includes self-debugging and recursive error handling
- Phased code edits, tracking cascading changes to support initial and downstream post edits.
With an initial focus on Java based workloads, this release helps engineers automatically evaluate, maintain and test application improvements and dependency changes. The AppFactor methodology keeps the engineer in control of the automation by generating a pull request, which can be reviewed before completion.
A number of Enterprise scale customers have already joined the Early Access Program to realize the promise of using GenAI to automate their application refactoring, allowing them to leverage all of the latest cloud-native compute capabilities.
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