AI capabilities
What is Learns From Feedback?
Adapting future findings based on which comments your team accepted, fixed or dismissed.
Every dismissed finding is a signal. A tool that records those decisions can stop repeating rejected findings and start matching your team's actual standards rather than a vendor default.
Without it, tuning is manual forever, meaning you suppress the same rule in every new repository. With it, precision should improve over weeks of use, which is worth verifying rather than assuming.
Top 16 Learns From Feedback tools
Every tool in the directory documented as supporting Learns From Feedback, 12 fully, 4 partially, with what its own documentation says.
Support is not the same as parity, since some implementations are narrower in scope, gated to a higher plan tier, or maintained only for existing customers. The note under each tool is what its own documentation describes.
- 1
Thumbs up/down reaction counts collected post-merge to tune the reviewer; no immediate re-review.
- 2CodeAnt AIPR Review
Saves developer disagreement as a 'learning' - customized instruction preventing repeat suggestions
- 3
Learnings database built from chat replies; editable, scoped, optional admin approval delay
- 4
Learned rules auto-generate from team GitHub activity and are auto-enabled or disabled over time.
- 5DeepSourceQuality Platform
Marking an AI Review issue a false positive opens a comment modal that feeds back into AI Review accuracy.
- 6GitHub Advanced SecurityCode Security Platform
Feedback on Copilot code-review comments improves later suggestions; no documented loop for Autofix or CodeQL
- 7GreptilePR Review
Learns from PR comments, replies, thumbs reactions and commit-based checks of addressed comments
- 8QltyQuality Platform
False-positive ignore reasons are recorded; aggregated issue data trains issue prioritization and grouping.
- 9
Rule Miner mines accepted review comments; Auto best practices learns from accepted suggestions
- 10
Memories auto-generated from admin triage feedback; autotriage uses the org's triage history; requires Multimodal.
- 11SnykCode Security Platform
Thumbs up/down on breakability PR comments and a SAST/DAST correlation feedback loop improve accuracy.
- 12
Gitar persists dismissed and resolved findings across review iterations, learning from developer replies.
- 13
False-positive marks are 'considered but re-checked'; editing the threat model changes future scan prioritization only.
- 14
Triage state and comments persist across scans on recurring instances; no documented model learning from feedback.
- 15
Delivery-risk ML self-adjusts thresholds as developers gain experience; no learning from review feedback
- 16
memory_config enables persistent per-repository memory; feedback forms collected, no documented review-tuning loop.
What to look for
- Whether learning is per-repository, per-organisation or global
- How long adaptation takes to show measurable effect
- Whether learned suppressions are visible and reversible
- Whether your feedback trains models shared with other customers
Related terms
FAQ
What is Learns From Feedback?
Adapting future findings based on which comments your team accepted, fixed or dismissed.
How many tools support Learns From Feedback?
16 of the 20 tools tracked in this directory support Learns From Feedback, 12 fully and 4 partially, including Claude Code, CodeAnt AI, CodeRabbit, Cursor (Bugbot), DeepSource. Support is not the same as parity, since some implementations are narrower in scope, gated to a higher plan tier, or maintained only for existing customers. The note under each tool is what its own documentation describes.
What should you look for in Learns From Feedback?
Whether learning is per-repository, per-organisation or global. How long adaptation takes to show measurable effect. Whether learned suppressions are visible and reversible. Whether your feedback trains models shared with other customers.