Episode Details

(00:00:00) The Case for AI-Powered Java Modernization
(00:00:26) The Legacy Java Dilemma
(00:01:47) AI-Driven Modernization Process
(00:04:22) The Assessment Phase: Exposing Technical Debt
(00:12:39) Cloud Migration and Cost Optimization
(00:17:06) The Results and Benefits of Automated Modernization
(00:20:53) Closing Thoughts and Call to Action

In this episode of M365.fm, Mirko Peters shows why manually upgrading legacy Java apps is unpaid penance — and how AI‑driven modernization can take you from Java 8 on AWS to Java 21 on Azure with receipts instead of heroics.

WHAT YOU WILL LEARN

• Why manual Java modernization is slow, error‑prone, and always behind on CVEs and tech debt
• How to inventory a legacy Java 8 Spring/Maven stack with drifted POMs, pinned dependencies, and brittle CI
• What Java 21 actually buys you: virtual threads, better GC, and a more stable platform for concurrency and performance
• How an AI agent builds a concrete plan: CVE remediation, dependency upgrades, OpenRewrite recipes, and cloud‑readiness checks
• How to move from AWS to Azure (App Service or Azure Spring Apps + Azure SQL) with proper bindings, Key Vault, and managed identities
• Why every action must land in Git as small, reviewable commits with SBOMs, scanner outputs, and full audit trail

THE CORE INSIGHT

Most teams think they “know” their legacy stack; the AI assessment proves they don’t. Forked parent POMs, transitive dependency roulette, duplicate logging bridges, and quiet CVEs all hide in plain sight until a structured agent inventories them. The real shift is from heroic, manual fixes to a loop where the agent proposes code changes, dependency bumps, and infra tweaks — and you approve them in Git with evidence attached.Mirko walks through how the agent: scans code, build files, plugins, Docker bits, and config; maps CVEs to real reachability; runs OpenRewrite recipes for Java 21; flags cloud anti‑patterns like stateful disk writes and hard‑coded secrets; and produces a plan that security, platform, and finance can all live with. You’ll hear why the most powerful slide in the deck was the cost and risk baseline: compute waste, CVE counts, and migration impact all quantified before a single line of code changed.Once the plan is approved, the agent stops talking and starts doing: applying recipes, fixing APIs, resolving dependency hell, regenerating SBOMs, and rerunning scanners in a tight loop until builds are green. From there, it containers the app, wires Azure hosting, connects to Azure SQL, and sets up CI/CD with staged rollouts and policy gates — all as traceable commits instead of 2 a.m. shell scripts.

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