AI-Augmented SDLC for Legacy Migration

// CASE RECORD

The problem

A legacy transaction-processing platform carries thousands of stored procedures. Migrating them by hand is slow, error-prone, and ties up senior engineers for weeks at a time — which slows feature delivery and raises defect risk for every downstream product squad that depends on the platform.

What I built

A tightly-bounded proof of concept: an AI-augmented software-development lifecycle that assists the riskiest, most repetitive parts of migration — specification, code, and tests — with humans firmly in the loop. Behavioral specs and test oracles anchor what “comparable or better quality” means, so speed is never bought at the cost of correctness. It runs safely in non-production first, with data governance over anything used for training.

Where it stands

An active build. The measures were defined up front, and the case reports against them after: PR cycle time from open to merge, procedures migrated per sprint, and defect escape rate — defects that slip past review into later stages. The target, labeled as a target: cut cycle time per procedure by 30–50% and free senior engineering time for higher-leverage work. If the pipeline proves out, it becomes a repeatable pattern other teams can adopt for their own legacy migrations — a small win designed to compound.