code-review-contextualizing

Understanding codebase context for code reviews involves retrieving semantic information like function callers, module imports, and dependency impacts. This process maps data flow and checks schema alignment to prevent hallucinated review feedback.

Tracing data flow and analyzing dependency impacts requires mapping the origin, destination, and transformations of data relevant to your diff. You perform graph-based analysis to assess direct, reverse, and transitive dependencies to determine the blast radius of modifications.

Symbol intelligence in graph-based code analysis is the identification of callers, importers, and overrides of modified symbols. It helps reviewers understand the potential impact of modifications to core utility functions by tracking referential and lexical traversal.

Yes, you can check database schema and API contract consistency during a code review by verifying schema and contract alignment. This process ensures that code modifications remain consistent with interconnected database schemas and API contracts.

The best way to find all callers of a modified function is using referential traversal to identify importers and overrides. This gathers information on all other functions that call the modified symbol, helping you understand the potential impact of the modification.

AI hallucinates during code reviews of complex interconnected changes due to a lack of surrounding codebase context. Providing semantic information like data flow maps and dependency graphs enables the agent to perform accurate, non-hallucinated reviews.