AI Agents Refactored Street Fighter III Codebase
Autonomous agents improved a massive game codebase through a three-week optimization process.
Updated on Sept. 30, 2026 in eSports
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Coding agents successfully refactored a 300,000-line C codebase belonging to Street Fighter III: 3rd Strike. The project resulted in a perfect Code Health score of 10.0 following significant structural improvements.
Why it matters
This demonstration highlights the capability of autonomous agents to handle complex legacy software maintenance at a fraction of human-driven costs. By utilizing automated replay-trace harnesses, researchers proved that AI can safely perform large-scale refactoring while maintaining game logic integrity.
Agents completed 2,903 commits across 726 files to modify 252,055 lines of code for a total token cost of $4,000. The process yielded 22 unique refactoring recipes across 54 separate pull requests.
The players
CodeScene
This organization specializes in software engineering intelligence and predictive analysis for codebase quality.
Lund University
This academic institution is slated to conduct future research studies regarding implementation speeds for codebases.
The details
The project utilized a CodeHealth MCP Server to provide a deterministic score for the agents to optimize during the process. A replay-trace harness verified the integrity of the code by comparing the rollback state hash frame by frame after every change.
Timeline
The refactoring process occurred over a three-week period.
CodeScene published the findings of the case study in September 2026.
Season Trajectory
This project sets a new benchmark for automated maintenance in the competitive gaming and eSports tech ecosystem. By accelerating the cleanup of legacy code, it significantly reduces the technical debt that often slows down game modifications and platform updates.
The study forecasts a 70% reduction in AI-induced defects, suggesting higher stability for future software-based gaming tools. It also projects 45% less token waste, indicating more efficient AI deployment for technical tasks.
The takeaway
Autonomous agents can effectively manage and improve massive legacy codebases without manual intervention. Developers looking to optimize their own projects should focus on deterministic scoring systems and automated rollback testing to ensure quality.
Further reading
For more on the intersection of gaming technology and software development, visit eSports.
Source note: This article includes information reported by InfoQ.
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