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    <title>InfoQ - Static Analysis</title>
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      <title>Alibaba Open Sources OpenCodeReview for AI-Assisted Code Review</title>
      <link>https://www.infoq.com/news/2026/09/alibaba-opencodereview/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Static+Analysis</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/09/alibaba-opencodereview/en/headerimage/generatedHeaderImage-1789473748016.jpg"/&gt;&lt;p&gt;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.&lt;/p&gt; &lt;i&gt;By Renato Losio&lt;/i&gt;</description>
      <category>Agentic AI Architecture</category>
      <category>Static Analysis</category>
      <category>Large language models</category>
      <category>Agents</category>
      <category>Code Reviews</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Architecture &amp; Design</category>
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      <pubDate>Sun, 20 Sep 2026 11:57:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/09/alibaba-opencodereview/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Static+Analysis</guid>
      <dc:creator>Renato Losio</dc:creator>
      <dc:date>2026-09-20T11:57:00Z</dc:date>
      <dc:identifier>/news/2026/09/alibaba-opencodereview/en</dc:identifier>
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    <item>
      <title>DoorDash Uses Multi Agent LLMs to Clean up 60,000 Feature Flags</title>
      <link>https://www.infoq.com/news/2026/09/doordash-feature-flag-cleanup/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Static+Analysis</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/09/doordash-feature-flag-cleanup/en/headerimage/generatedHeaderImage-1788418529526.jpg"/&gt;&lt;p&gt;DoorDash built a multi-agent LLM system to automate stale feature flag cleanup across more than 60,000 flags and 623 repositories. The workflow combines live experimentation data through MCP, engineer approval, isolated Git worktrees, parallel agents, and automated validation. In an evaluation of 50 flags, 45 produced usable pull requests at an average of 13.8 minutes and $4.79 per cleanup.&lt;/p&gt; &lt;i&gt;By Leela Kumili&lt;/i&gt;</description>
      <category>Feature Injection</category>
      <category>A/B Testing</category>
      <category>Developer Experience</category>
      <category>Experiment Driven Development</category>
      <category>Static Analysis</category>
      <category>AI Assisted Coding</category>
      <category>Automation</category>
      <category>Model Context Protocol (MCP)</category>
      <category>Productivity</category>
      <category>AI Architecture</category>
      <category>git</category>
      <category>AI Coding</category>
      <category>Large language models</category>
      <category>github</category>
      <category>Agents</category>
      <category>Feature Toggle</category>
      <category>DevOps</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>news</category>
      <pubDate>Fri, 18 Sep 2026 13:50:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/09/doordash-feature-flag-cleanup/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Static+Analysis</guid>
      <dc:creator>Leela Kumili</dc:creator>
      <dc:date>2026-09-18T13:50:00Z</dc:date>
      <dc:identifier>/news/2026/09/doordash-feature-flag-cleanup/en</dc:identifier>
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