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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. ...

Jump to TL;DR This week, our focus shifted from theory to implementation as we began shaping our first agentic refactoring prototype. 1. Visualising the Workflow Last Updated: 25 July After exploring multi-agent architectures and SkillsBuild certifications, we started formalising how our AI-powered refactoring system should operate end-to-end. Thanks to Vamsi, who drafted the base structure, we’ve now consolidated our ideas into a clear, modular agentic workflow (click on image to enlarge): ...

Jump to TL;DR Welcome to the official blog for our UCL MSc project in collaboration with IBM! Over the coming weeks, we’ll share progress updates, key learnings, and reflections as we build an AI-powered tool to help modernise legacy software systems. 1. Introduction To build an effective AI tool for refactoring legacy software, we knew we had to establish a shared understanding of key AI concepts, fast. Instead of diving straight into building, our team took a structured approach: we enrolled in IBM SkillsBuild’s AI and Collaboration pathways. These courses gave us a practical introduction to current AI tools and practices, with a special focus on the capabilities of IBM watsonx and Granite models. ...