Cadre

Capstone 09 — Code Migration Agent (Repo-Level Language / Runtime Upgrade)

Amazon's MigrationBench (Java 8 to 17) and Google's App Engine Py2-to-Py3 migrator set the 2026 bar. Moderne's OpenRewrite does deterministic AST rewrites at scale. Grit targets the same problem with codemod-style DSL. The production pattern combines both: a deterministic substrate for safe rewrites plus an agent layer for the ambiguous cases, a sandbox for per-branch builds, and a test harness that flips green before the PR opens. The capstone is to migrate 50 real repos and publish a pass rate with a failure taxonomy.

Capstone 30 hr Python (agent), Java / Python (targets), TypeScript (dashboard)

Prerequisites: Phase 5 (NLP), Phase 7 (transformers), Phase 11 (LLM engineering), Phase 13 (tools), Phase 14 (agents), Phase 15 (autonomous), Phase 17 (infrastructure)

#Problem

Large-scale code migration is one of the cleanest production applications of 2026 coding agents. The ground truth is obvious (does the test suite pass after the migration?), the rewards are real (a Java-8 fleet migration is a headcount-scale project), and the benchmarks are public (MigrationBench 50-repo subset). Moderne's OpenRewrite handles the deterministic side. The agent layer handles everything OpenRewrite recipes cannot: ambiguous rewrites, build-system drift, long-tail syntax, transitive dependency breakage.

You will build an agent that takes a Java 8 repo (or Python 2 repo) and produces a green-CI migrated branch. You will measure pass rate, test-coverage preservation, cost per repo, and build a failure taxonomy. The side-by-side against a deterministic-only baseline tells you where the agent's value actually lives.

#Problem

Keep going. The rest of this lesson is one step away.