Java Application Modernization - Series Introduction

Hello everyone. I'm Ian. I'm joining you from Redmond, Washington, Microsoft HQ, and I am so excited that you're joining us on this brand new series modernize Java apps with AI. Before we get started, let me tell you about one of the most surprising modernization failures in recent history. Does anyone by any chance know what this is? If you don't, this is the Louvre heist, and it happened a few months ago. And at first glance, most people would say it's a physical security issue. I mean, they took a ladder, they broke a window, they got in. But the deeper story is actually about systems that age quietly. Technology that still functioned, but no longer met modern security assumptions. The system wasn't broken per se, it was working exactly as designed. However, weak passwords, outdated OS, delayed patching, temporary exceptions which became permanent, all added to a layered exposure issue. Audits had flagged these issues years earlier, but nothing changed because of complexity, cost, fear of downtime, and this whole idea that everyone's afraid to touch something that's working until it doesn't. And this is what it feels like when trying to upgrade or modernize enterprise systems. And this is the problem that we're here to solve today using the GitHub Copilot Modernize extension to put out the fire before we even even smell the smoke, to be proactive. Welcome to the series where we're taking a deep dive into how GitHub Copilot can serve as a powerful agentic tool for your modernization workflows. In this series, we'll walk through the modernizer modernization journey step by step from assessment and planning, upgrading Java frameworks, database migration and modernization, customizing modernization tasks, containerization, as well as CLI and pipeline automations. Each episode is designed to be 5 to 7 minutes long, focused on the practical fundamentals such that you are set up and empowered to start your own modernization journey. The best way to put what we are experiencing right now is an AI gold rush. 94% of executives say that AI is critical, but unfortunately, only 28% have scaled pilots into production. There is so much ambition, so much money being thrown at AI, but the execution gap is huge. And that's where modernization comes in. And this promise is sparking a renewed interest in the modernization process and putting greater pressure on developers and IT organizations to move faster, to modernize at a greater pace than ever before. We see more demand than ever before to modernize decades of Java applications, to move legacy systems to the cloud, and to ensure that every app is ready for the AI generation. From our perspective, becoming frontier means that you are built on the foundation to support the future of your business. This moves modernization from something you would like to do into a key step that is both necessary and critical for AI adoption. Becoming a frontier firm isn't optional. It's about having the foundation to build what's next. And this is where the tool comes in. It provides a platform that streamlines the modernization of enterprise-level applications. It allows you to quickly perform end-to-end code assessments to discover core issues and insights about your application. You're able to use AI agents to upgrade your application into versions and associated frameworks which you want to increase app performance. You're able to make your apps cloud-ready by automatically updating your code and dependencies to run on Azure. And you're able to leverage industry best practices and customize them for your organizational needs. On a high level, this is how the modernization loop looks like. Every customer project, regardless of stack, boils down into these four phases. The first is assessment. It helps you get clarity on the dependencies, frameworks, patterns, and even CVEs. The second being upgrade, it allows you to move to supported frameworks and versions or target frameworks and versions. Migration, you shift to the cloud or to new platforms, and the fourth is testing and deployment. You validate, tune, and ship your code. I want you to notice how this mimics what a team would typically do. We are, however, using AI to supercharge the process. Also notice how it's an iterative loop. It doesn't terminate, it continues, and it can sometimes take multiple go-arounds at testing and validating changes, but it will iterate like a human to get it done. The magic is that AI is doing it for you, and it's being done a lot quicker. We just looked at a simple loop. However, this is now an exploded loop of what an enterprise-level application might go through. This whole process comes together. Portfolio assessment tools, GitHub issues, AI agents, developer tools, and organizations usually enter this workflow in one of two ways. The first is that they already know what they want to modernize, in which case you start directly with the app modernization tool. However, more often than not, you don't know where to start, which is entry point B. In which case, you can start with portfolio assessment tools such as Dr. Migrate, CAST, or Azure Migrate. These scan your on-premise systems and recommend which apps are best suited as modernization candidates. Once the assessment is complete, GitHub issues are automatically created. So, every modernization opportunity becomes something actionable. Developers are able to pick up issues inside tools they already use, Visual Studio, VS Code, IntelliJ, whatever IDE you prefer. There's also no context switching. Modernization integrates with existing workflows, and the magic is when AI agents help with the repetitive tasks, which can sometimes take days. And these tasks include identifying outdated frameworks and versions, updating dependencies, automatically fixing issues and CVEs, migrating configurations, generating deployment assets, and containerization. And this is not a comprehensive list. GitHub Copilot is able to do so much more. And now you are able to focus on decisions, not the mechanics of those decisions. A process that used to take days can now be done in practically hours. And once upgraded and validated, you're easily able to move into staging and production rollout. Our goal has been that by connecting portfolio tools, GitHub issues, AI-powered workflows, you are able to modernize much faster than ever before with far less manual effort. And the whole process stays inside the developer workflow you already use. This series is designed to walk you through each part of this iterative loop. In the next episode, we'll start with application assessment using the GitHub Copilot app modernization extension to see how AI agents are able to supercharge perhaps one of the hardest parts of modernization, understanding what the heck is going on. Okay, that's it for now. I'll see you in the next episode.