Artificial intelligence change impact analysis traces relationships between a proposed change and the wider software system. It can inspect dependencies, call paths, tests, configuration, ownership, architecture, pull requests, and incident history to identify likely consequences before implementation.
The analysis is only as reliable as its context and dependency evidence. Dynamic behavior and undocumented decisions can remain invisible. Results should therefore guide investigation and test selection rather than act as proof that unlisted areas are unaffected.
