AI review comment deduplication compares findings from several models or rules and identifies comments describing the same underlying issue. The system can retain the clearest or highest-confidence version and preserve provenance from the contributing reviewers.
Deduplication reduces cognitive load and prevents repeated feedback from appearing more important merely because several agents found it. Similarity rules need evaluation so distinct issues are not incorrectly merged.
ELI5
Review comment deduplication finds automated review comments that describe the same underlying issue and combines them before a person sees them. It reduces repeated noise while preserving which tools or models contributed the evidence.
For example, three reviewers may all warn that one input lacks validation using slightly different wording. The system can keep the clearest comment and record all three sources, but it must avoid merging separate problems that only look similar.
Why deduplicate AI code review comments?
It reduces repeated feedback, reviewer fatigue and misleading severity caused by several agents reporting the same problem.
What can go wrong during comment deduplication?
An overly broad similarity rule can merge distinct defects, while a weak rule can leave redundant comments in the review.
