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This case study demonstrates the emergence of autonomous coding agents capable of performing complex, large-scale refactoring tasks by autonomously developing domain-specific optimization recipes. The results highlight that model capability and deterministic verification harnesses are critical for scaling AI software engineering beyond simple code completion to substantive architectural improvements.
Agents Refactor 300K Lines in Three Weeks, and Practitioners Ask What it Proves
CodeScene has published a case study in which coding agents refactored 300,000 lines of C over three weeks for roughly $4,000 in tokens, verified by a frame-by-frame replay harness. The agents built a playbook of codebase-specific recipes along the way. Practitioners have questioned the scope, the metric, and how much of the result depends on the harness. By Steef-Jan Wiggers
CodeScene has published a case study in which coding agents refactored a 300,000-line C codebase over three weeks, at a token cost of roughly $4,000. The work produced 2,903 commits across 726 files, modified 252,055 lines, and moved the codebase from a Code Health score of 5.6 to 10.0. The codebase is Street Fighter III: 3rd Strike, taken from an open-source decompilation. Adam Tornhill, CodeScene's founder and the author of Your Code as a Crime Scene, wrote that this was the first time he had seen what he called superhuman AI performance at scale, after three decades working on large…
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