AI capabilities

What is learning from feedback?

Adapting future findings based on which comments your team accepted, fixed or dismissed.

By Cláudia FernandesUpdated 2026-10-02

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 stays manual, often meaning the same rule suppressed in every new repository. With it, precision should improve over weeks of use, which is worth verifying rather than assuming. The risk runs the other way too: if a team dismisses valid findings under deadline pressure, the tool can learn to stop raising them.

What to look for

Whether learning is per-repository, per-organization 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

Best 16 code quality, security and code review tools that learn from feedback

Every tool in the directory whose documentation covers this, 12 fully and 4 partially. Full support is shown first. Shown in a random order that changes on every visit, so no tool gets an advantage from its position.

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.

AI Coding Agent

Claude Code

Thumbs up/down reaction counts collected post-merge to tune the reviewer; no immediate re-review.

PR Review Tool

CodeAnt AI

Saves developer disagreement as a 'learning' - customized instruction preventing repeat suggestions

PR Review Tool

CodeRabbit

Learnings database built from chat replies; editable, scoped, optional admin approval delay

PR Review Tool

Cursor (Bugbot)

Learned rules auto-generate from team GitHub activity and are auto-enabled or disabled over time.

Code Quality Platform

DeepSource

Marking an AI Review issue a false positive opens a comment modal that feeds back into AI Review accuracy.

Code Security Platform

GitHub Advanced Security

Feedback on Copilot code-review comments improves later suggestions; no documented loop for Autofix or CodeQL

PR Review Tool

Greptile

Learns from PR comments, replies, thumbs reactions and commit-based checks of addressed comments

Code Quality Platform

Qlty

False-positive ignore reasons are recorded; aggregated issue data trains issue prioritization and grouping.

PR Review Tool

Qodo

Rule Miner mines accepted review comments; Auto best practices learns from accepted suggestions

Code Security Platform

Semgrep

Memories auto-generated from admin triage feedback; autotriage uses the org's triage history; requires Multimodal.

Code Security Platform

Snyk

Thumbs up/down on breakability PR comments and a SAST/DAST correlation feedback loop improve accuracy.

Code Quality Platform

SonarQube

Gitar persists dismissed and resolved findings across review iterations, learning from developer replies.

AI Coding Agent

ChatGPT / Codex

False-positive marks are 'considered but re-checked'; editing the threat model changes future scan prioritization only.

Code Security Platform

Checkmarx One

Triage state and comments persist across scans on recurring instances; no documented model learning from feedback.

Code Quality Platform

CodeScene

Delivery-risk ML self-adjusts thresholds as developers gain experience; no learning from review feedback

AI Coding Agent

Gemini Code Assist

memory_config enables persistent per-repository memory; feedback forms collected, no documented review-tuning loop.

AI Code Review Bring Your Own Model MCP Server AI Code Governance Conversational Review Code Excluded From Training

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