Refactoring

Jump to TL;DR We’ve now reached a point where the platform can take a Java file, run it through the full workflow, and return a refactored version with no manual steps in between. That’s thanks to two new agents, coverage integration, and automated file handling, all stitched together into one continuous pipeline. 1. Code Reviewer Agent The Code Reviewer is our new quality gate between transformations, and is now fully implemented. When the Code Transformer makes changes, the Reviewer checks them for correctness, quality, and whether they actually match the intended refactoring strategy. ...

Jump to TL;DR Following last week’s groundwork in static analysis and semantic retrieval, our focus this week has shifted toward multi-model orchestration, LLM infrastructure, and preparing for our upcoming IBM showcase on the 16th. 1. Unified Provider Interface To enable flexible experimentation and future-proofing, we’ve implemented a unified interface for interacting with multiple LLM providers. Our abstraction currently supports Ollama, vLLM, and WatsonX, but, following the Open-Closed Principle, we’ve made sure to facilitate the addition of new ones with no friction. ...

Jump to TL;DR With the foundation in place from previous weeks, our focus has now shifted toward rapid iteration and structural refinement. We’ve transitioned away from early experiments in Jupyter Notebooks (used last week during our move from LangFlow to LangGraph) to a more robust and modular Python architecture. This will give us finer control over our agent workflows and ease integration across components. 1. SonarQube - Automated Static Analysis We’ve built a fully scripted toolchain for running SonarQube scans on repositories, including intelligent cloning logic that ensures each project is up to date. This scanner will soon feed directly into our Pattern Scanner Agent, providing high-quality static analysis as part of the agent’s reasoning process. ...