Case Study: The first contribution of DevBird to rspack

When the rspack team needed to migrate their codebase, they tried AI-powered development tools to handle the repetitive work. What started as a performance evaluation turned into a compelling demonstration of DevBird's capabilities, particularly its autonomous CI failure fix system.

The Challenge

The rspack team had a specific migration task to complete across their codebase. It was the kind of systematic change that seems perfect for AI automation: well-defined, repetitive, but requiring attention to detail and context awareness.

Initial Attempts

GitHub Copilot's Result

The team first tried GitHub Copilot, which generated a pull request (#11966). Unfortunately, the results were unsatisfactory—the PR quality didn't meet the team's standards and wasn't viable for merging.

Testing DevBird (formerly AutoDev)

Note: At this time, DevBird was still named AutoDev before our recent rebrand.

Interested in evaluating alternative AI coding solutions, the rspack team reached out to test DevBird. I ran DevBird on their behalf by:

1. Forking the repository to my personal account

2. Configuring DevBird for the task

3. Letting DevBird process the migration

The initial result: PR #2 on the fork

Note: I enabled CompositeTask feature for DevBird on this task and it produced a single-node task graph. If a task is complex enough,DevBird will create multiple PRs. Still, a single-node task graph generates extremely good prompt for the actual run, and it’s the key.

The DevBird Advantage: Autonomous Error Recovery

Here's where DevBird's architecture proved its value. Like Codex Cloud Web (which was also tested), DevBird's initial code didn't pass CI on the first attempt. However, DevBird's automatic PR fix feature detected the CI failure and autonomously corrected the issues.

After receiving confirmation from the Rspack team member that the PR was "perfect," we submitted it to the main repository: PR #11978

Codex Cloud Web Comparison

Codex Cloud Web was also tested on the same task. While it generated initial code, it encountered CI failures. The team member attempted to recover by commenting @codex Fix CI, which triggered Codex to work on the problem for over 40 minutes. Unfortunately, the CI still failed after this extended attempt.

At this point, the team member concluded that further attempts weren't worthwhile and merged the DevBird-generated PR instead.

Key Takeaways

1. Autonomous Recovery Matters

The ability to self-correct isn't just a nice-to-have feature—it's essential for production use. Both DevBird and Codex failed CI initially, but DevBird's automatic fix capability meant it could recover without human intervention.

2. Time to Resolution

• DevBird: Autonomous fix, ready for merge

• Codex Cloud Web: 40+ minutes of attempted fixes, still failed

• GitHub Copilot: Initial PR not viable

The time difference stems from the design differences between these tools. With DevBird, you can use your existing GitHub Actions code to configure the development environment for AI agents, significantly reducing AI costs and the time required to set up the runtime environment.

3. Production-Ready Results

The Rspack team's feedback was clear: the DevBird PR was "perfect" and ready for merge. This real-world validation from an active open-source project demonstrates DevBird's capability to handle actual development tasks.

Conclusion

This case study illustrates a crucial distinction in AI-powered development tools: generating code is only half the battle. The ability to iterate, test, and fix issues autonomously is what separates tools that assist developers from tools that can actually complete tasks end-to-end.

DevBird's architecture—with its built-in PR monitoring and automatic fix capabilities—proved essential in delivering production-ready results for the Rspack team. While multiple AI coding tools attempted the same task, only DevBird successfully delivered a mergeable solution.

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Interested in seeing how DevBird can help your team? Get started today to learn more about autonomous error recovery and other features.