图片来源： CodeRabbit CodeRabbit 完成 6000 万美元融资，这家成立两年的 AI 代码审查初创公司估值达 5.5 亿美元 投资者对这家初创企业的增长态势感到兴奋。周二， CodeRabbit 宣布完成 6000 万美元 B 轮融资，公司估值达 5.5 亿美元。 本轮融资由 Scale Venture Partners 领投，英伟达风投部门 NVentures 及包括 CRV 在内的现有投资方跟投，使得公司融资总额升至 8800 万美元。 CodeRabbit 正帮助 Chegg 、 Groupon 、 Mercury 等 8000 多家企业节省代码审查这项 notoriously frustrating 任务的时间——随着 AI 生成代码的兴起，这 项工作变得更加耗时。 哈乔特 ·吉尔（ Harjot Gill ）在 2018 年将他的第一家初创公司 Netsil 出售给 Nutanix 后，又与他人共同创立了可观测性初创公司 FluxNinja 。几年后，他 注意到一个有趣的现象。 "我们有一个远程工程师团队，他们开始在 GitHub Copilot 上采用 AI 代码生成功能，" ...

Core Viewpoint - The article discusses the evolution and challenges of AI code review tools in the software development industry, highlighting the need for a collaborative approach between AI and human reviewers to ensure code quality and security [2][3][24]. Group 1: Current State of AI Code Review Tools - There are over 20 AI-assisted coding tools available, claiming to improve code review efficiency by up to 300% [2]. - Some AI tools overlap significantly with traditional static code analysis tools, leading to debates about their actual effectiveness [2][3]. - Developers face issues with false positives from AI tools, which can lead to unnecessary code modifications that overlook performance or security risks [3][4]. Group 2: Layered Review System - A three-tiered review system is emerging: basic syntax and compilation errors handled by traditional tools, middle-layer quality attributes assessed by AI, and business logic verified by human reviewers [4][6]. - AI tools excel in identifying complex code quality issues, such as performance bottlenecks and security vulnerabilities, when combined with traditional analysis [5][6]. Group 3: Challenges and Adjustments in Code Review - Traditional code review methods need to adapt to AI-generated code, focusing not only on correctness but also on project suitability [8][10]. - The core capability of AI code review tools lies in understanding the code project and its intent, which is essential for assessing code logic [9][10]. Group 4: Future Directions and Recommendations - The future of code review will likely see increased automation, with AI handling basic details while human engineers focus on higher-level design and logic [24][25]. - A collaborative model where AI performs initial checks followed by human review is recommended to enhance accuracy and efficiency [27][28]. - AI tools should be designed to learn from team-specific coding styles and project contexts to provide more relevant suggestions [21][22].