InfoQ Homepage Code Reviews Content on InfoQ
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Alibaba Open Sources OpenCodeReview for AI-Assisted Code Review
Alibaba recently open-sourced OpenCodeReview, an AI-powered code review CLI that combines deterministic pipelines for file selection, bundling, and rule matching with an LLM agent for dynamic code analysis. It supports built-in checks for issues such as null-pointer exceptions, thread safety, XSS, and SQL injection.
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Rigorous Yet Sustainable Human Reviews in the AI Era
Mandatory AI checks paired with manual spikes for complex changes keep developers sharp. Teams can boost velocity by skipping peer reviews on low-risk PRs and using AI approvals, provided most developers are code owners and teams are small.
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DoorDash’s Flux Runs 130,000 Engineering Tasks through Cloud-Based Agents
DoorDash has moved engineering agent workloads from developer laptops to its Flux cloud platform. The platform automated 130,000 engineering tasks in one month and supports more than 25,000 automated code reviews weekly. Flux uses isolated Firecracker microVMs, an MCP gateway, reusable playbooks, and multiple invocation surfaces to run agent workflows with scoped access and centralized auditing.
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AI Code Review at Scale: LinkedIn's Multi-Agent Approach
At LinkedIn's scale, relying solely on human reviewers or simply putting an off-the-shelf AI reviewer in front of GitHub is not an effective way to manage PRs. To address this, LinkedIn engineers built a multi-agent AI code review platform that understands the organization's coding context, treats code review as production infrastructure, and minimizes hallucinations and low-signal feedback.
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How Code in the Age of Artificial Intelligence Becomes Write-Only and Disposable
Artificial intelligence (AI) makes all code write-only,. It’s too dense to read, and tests define the behaviour and become the documentation. Code is also disposable; it becomes easier to rewrite than to debug. Humans can't review AI-generated code at scale. Intent decouples from implementation; developers should focus on creativity.
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Rootly Drops Small PR Rule as Agentic AI Changes Code Review Economics
Incident management platform provider Rootly has published an account of its decision to drop its long-standing small pull request rule, arguing that the practice no longer serves its purpose now that AI agents generate most of its code. The company describes a shift from measuring PR size to assessing blast radius, with feature flags and rollback capability taking precedence over line counts.
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Dropbox Integrates MCP and Dash to Close the Gap between Security Design and Code Review
Dropbox has integrated Model Context Protocol (MCP) with its internal knowledge platform, Dash, to surface security design context during AI assisted code reviews. The system retrieves threat models and security requirements for pull requests, helping reviewers validate implementation against design intent. An InfoQ Q&A explores the architecture and key lessons learned.
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AI Is Moving up the Software Lifecycle: from Code Review to PRD Governance
Technology companies are extending AI beyond code generation into earlier stages of the software lifecycle, including PRD validation, design inputs, and code review. Initiatives from Uber, DoorDash, and Cloudflare highlight a shift toward AI-driven governance layers that evaluate engineering artifacts before implementation while preserving human oversight across the development pipeline.
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Pullfrog AI: Open-Source CodeRabbit Alternative Powered by GitHub Actions
Pullfrog is an open-source AI-powered GitHub bot by Colin McDonnell, designed for automation in GitHub Actions. It supports a model-agnostic approach, allowing integration with various LLM providers. Key features include orchestration for pull request reviews, issue triage, and CI remediation, all managed within GitHub's environment. The tool operates with a bring-your-own-key model for access.
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Meta Reports 4x Higher Bug Detection with Just-in-Time Testing
Meta introduces Just-in-Time (JiT) testing, a dynamic approach that generates tests during code review instead of relying on static test suites. The system improves bug detection by ~4x in AI-assisted development using LLMs, mutation testing, and intent-aware workflows like Dodgy Diff. It reflects a shift toward change-aware, AI-driven software testing in agentic development environments.
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Anthropic Introduces Agent-Based Code Review for Claude Code
Anthropic has introduced a new Code Review feature for Claude Code, adding an agent-based pull request review system that analyzes code changes using multiple AI reviewers.
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HubSpot’s Sidekick: Multi-Model AI Code Review with 90% Faster Feedback and 80% Engineer Approval
HubSpot engineers introduced Sidekick, an internal AI powered code review system that analyzes pull requests using large language models and filters feedback through a secondary “judge agent.” The system reduced time to first feedback on pull requests by about 90 percent and is now used across tens of thousands of internal pull requests.
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GitLab 18.8 Marks General Availability of the Duo Agent Platform
GitLab 18.8 brings a number of new features, including GitLab Duo Planner Agent, GitLab Duo Security Analyst Agent, auto-dismiss irrelevant vulnerabilities, and more. With this release, the GitLab Duo Agent Platform, enabling organizations to orchestrate AI agents, reaches general availability.
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How Pair Programming Enhanced Development Speed, Focus, and Flow
Ola Hast and Asgaut Mjølne Söderbom gave a talk about continuous delivery with pair programming at QCon London. Their team uses pair and mob programming with TDD; there are no solo tasks or separate code reviews. This approach boosts code quality, reduces waste, and enables the sharing of knowledge. Frequent breaks help to maintain focus and flow.
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Learning from Embedded Software Development for the Space Shuttle and the Orion MPCV
Software development is much different today than it was at the beginning of the Space Shuttle era because of the tools that we have. But the art and practice of software engineering has not progressed that much since the early days of software development. Compilers are much better and faster, and debuggers are now integrated into development tools, making the task of error detection easier.