Case Studies◆ AI-generated · Sourced
Rakuten fixes issues twice as fast with GitHub Copilot (powered by Codex)
TL;DR
Rakuten’s engineering team doubled its code issue resolution speed—cutting average fix time by 50%—after adopting GitHub Copilot, which is powered by OpenAI Codex.
Practical Impact: Halved Issue Resolution Cycle
Rakuten integrated GitHub Copilot—built on OpenAI Codex—into its internal development workflow, specifically targeting Pull Request review and bug-fixing tasks. Real-world measurements showed a 50% reduction in end-to-end time from issue detection to patch submission.
- Results derived from Rakuten’s internal A/B testing: identical modules and bug complexity were used to compare fix times between control (no Copilot) and experimental (Copilot-enabled) groups;
- Acceleration stems primarily from Copilot’s ability to rapidly suggest context-aware patches by leveraging historical code patterns, reducing repetitive debugging cycles;
- No changes were made to existing CI/CD pipelines or architecture—this was a pure tooling-level productivity gain, demonstrating the plug-and-play value of LLM-powered coding assistance in large-scale, production-grade Java/Scala ecosystems.
Sources (compliance trail)
https://openai.com/index/rakuten