What Does a Legacy System Teach Us?
summary
The talk examines techniques for evolving long‑running (legacy) systems by mining git commits, database constraints and commit history to detect change‑coupling and real domain boundaries. It describes extracting low‑hanging value objects and clusters into aggregates, then building read models (CQRS) and small application services; an example turns a three‑month brownfield tweak into a focused extraction of a leasing model that compiles and runs in production. Heuristics plus production usage and time are used to prioritize work and reduce risk.
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chapters (10)
- 0:00Introduction
- 1:06Legacy systems defined
- 3:57Legacy vs Greenfield differences
- 10:15Analyzing Legacy code
- 16:39Legacy advantages over Greenfield
- 26:52Business modeling in Legacy
- 33:11Analytical thinking in practice
- 41:57Heuristics and prioritization in Legacy
- 44:03Humility and patience with Legacy
- 47:14Summary and conclusions
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