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Code Scans

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Why This Matters

Devin's new Code Scans feature automates the tedious first steps of large engineering initiatives—investigating a codebase and turning findings into actionable pull requests—by using parallelized AI agents. This matters because it addresses a common bottleneck where broad goals like improving SEO or reducing technical debt stall due to the sheer effort of investigation and implementation, potentially saving significant engineering time as shown by early adopters.

Key Takeaways

Today we’re introducing Code Scans: a new way to turn broad engineering goals into concrete improvements across your codebase. Tell Devin what you want to achieve, and it helps you investigate what needs to change, evaluate the findings, and turn them into pull requests.

Many engineering tasks start with a specific change. Others start with an outcome:

Improve SEO

Reduce maintenance overhead

Make your application compile faster

These goals often stay on the backlog because the first step alone is a substantial project: investigating the codebase to figure out where to start. Additionally, identifying the opportunities is only half the work — someone still has to implement the changes, and many of them.

Code Scans helps with both.

Powered by Agentic MapReduce, the architecture we built for Devin Security Swarm, Code Scans breaks large investigations into focused batches, distributes them across parallel agents, and synthesizes their findings into one report. Devin then turns its findings into PRs ready to review.

Teams testing Code Scans ahead of launch are already seeing results:

Code Scans had a significant impact on the things we care most about: software quality, bug fixes, stability, performance, and compliance, including security and privacy. With an estimated 96% PR merge rate across several repos, we estimate we've saved over 700 engineering hours in our short testing timeframe. Digital Computational Pathology, Enterprise Informatics at Philips PR merge rate 96% Engineering hours saved 700+

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