# Bugbot 现可通过学习规则实现自我改进

- 来源：Cursor Blog
- 发布时间：2026-04-08 20:00
- AIHOT 分数：66
- AIHOT 标记：精选
- AIHOT 链接：https://aihot.virxact.com/items/cmosbbgu40074sloj8tx0lrbk
- 原文链接：https://cursor.com/blog/bugbot-learning

## 精选理由

AI code review 赛道卷了两年，Bugbot 78% 的解决率终于把第二名甩开 15 个点，关键不是分数而是它开始从真实 PR 反馈里自动学规则，做 code review 工具的该认真看看这套闭环逻辑。

## AI 摘要

Bugbot 的 bug 解决率已从 2025 年 7 月正式推出时的 52% 提升至近 80%，领先其他 AI 代码审查产品。其核心改进在于引入了规则学习机制，能够从实时代码审查反馈（如开发者反应、回复和人工评审意见）中自主学习，取代了原先依赖离线实验的更新模式。自测试版推出以来，已有超过 11 万个仓库启用该功能，生成了逾 4.4 万条规则。这些规则可根据信号积累被激活或禁用，帮助 Bugbot 更精准地识别问题。用户可在 Cursor Dashboard 中管理学习规则，以优化审查效果。

## 正文

Blog / product

When we launched Bugbot out of beta in July 2025, 52% of the bugs it identified were resolved by the time the relevant PR was merged, indicating the rest were false positives.

Today, the resolution rate is nearing 80%, 15 percentage points higher than the next-closest AI code review product.

AI code review productResolution ratePRs analyzed

Cursor Bugbot78.13%50,310

Greptile63.49%11,419

CodeRabbit48.96%33,487

GitHub Copilot46.69%24,336

Codex45.07%19,384

Gemini Code Assist30.93%21,031

We analyzed public repositories only. For each comment produced by an AI code review product, we checked to see if it was addressed by the time it merged using an LLM judge.

Up until now, improvements have been propelled exclusively by offline experiments: We tweak Bugbot, test to see if the change improves the resolution rate, and we ship it if it does.

But a strictly offline approach leaves a lot of training potential untapped. Bugbot reviews hundreds of thousands of PRs per day, and each review is a natural experiment that Bugbot can use to self-improve based on whether the developer acted or not on its report.

To harness those real-time signals, we've now enabled Bugbot to learn from past runs, transforming feedback from the live code review process into learned rules. Rules act as additional instructions that enable greater customization of Bugbot runs, helping Bugbot focus on specific issues, business context, and more.

Since launching learned rules in beta, more than 110,000 repos have enabled learning, generating more than 44,000 learned rules.

How learned rules work

Every merged PR contains a range of signals that Bugbot can use to self-improve and codify into rules. Three important signals are:

Reactions to Bugbot comments, where a downvote tells Bugbot the finding wasn't useful.

Replies to Bugbot comments, in which developers explain what was wrong or how the suggestion could have been better.

Comments from human reviewers, which flag issues that Bugbot missed.

Bugbot processes these signals into candidate rules that it continues to evaluate against incoming PRs. As signal accumulates, Bugbot can promote a candidate rule to active status where it begins influencing future reviews. Similarly, if an active rule starts generating consistent negative signal, Bugbot can disable it. You can also edit or delete rules directly in the UI.

We want Bugbot to catch every real bug, which requires a deep understanding of your codebase, patterns, and team's priorities. Learned rules are a big step in that direction, and they're part of our overall effort to make Bugbot continually self-improving.

Manage Bugbot learning in the Cursor Dashboard, where you can enable learned rules and run a backfill across recent PRs, or learn more in our docs.

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