aiqbench analysis
This case study demonstrates how AI agents can systematically refactor large-scale legacy codebases, transforming a task that previously took years into a three-week process using existing software as context. By leveraging modern code quality tools and iterative playbooks, the approach successfully eradicated technical debt while preserving functional correctness in a complex C project. The results highlight a significant shift in development economics, where token costs now rival only a fraction of traditional expert labor expenses for deep refactoring tasks.
Case Study: Refactoring at Scale with Agents
<div class="hs-featured-image-wrapper"> <a href="https://codescene.com/blog/case-study-refactoring-at-scale-with-agents" title="" class="hs-featured-image-link"> <img src="https://codescene.com/hubfs/Agentic_Refactoring_Streetfighter_blog_image.png" alt="Case Study: Refactoring at Scale with Agents" class="hs-featured-image" style="width:auto !important; max-width:50%; float:left; margin:0 15px 15px 0;"> </a> </div> <p style="font-weight: bold; font-size: 20px;"> </p>
Your existing code is an important part of any AI agent’s context. Consequently, poorly structured code and technical debt are considerable barriers to agentic coding. In the past, uplifting a legacy codebase used to be a risky and expensive endeavour. Many are the technical debt backlogs that never got priority. Even more numerous are the failed infamous project re-writes. This is where coding agents paired with modern code quality tools offer nothing short of a revolution. Let’s look at the proof from our recent case study. Our case study demonstrates agentic refactoring at scale on a real-world, non-trivial codebase: Street Fighter III: 3rd Strike. The codebase consists of 300k lines of C code. We refactor the whole codebase to eradicate any technical debt, bringing the code to a level where new features could be added safely with AI. Functional correctness…
Excerpt shown under the publisher's RSS terms · 140 words · full text and figures are on the publisher's site
Related coverage
Why AI Coding Agents Need a Deterministic Code Health Gate
CodeScene · Sep 15, 2026
Performance Profiling on AMD GPUs – Part 5: Profiling-Driven Kernel Optimization with an AI Code-Assist Tool
AMD · Jul 16, 2026
Presentation: Moving Mountains: Migrating Legacy Code in Weeks Instead of Years
InfoQ · Jun 12, 2026
Specification-first convergence with an AI coding agent: a case study of dismantling a core architectural invariant across 189 files in a 717k-line codebase with no test oracle and no human code review
arXiv · Aug 18, 2026