Characterizing the Quality Profile of AI-Generated C++ in Production

AI-generated C++ can introduce recurring inefficiencies that targeted feedback helps address. A study covering 3.52 million code changes at one enterprise found, compared with human-written code, more copying and memory-allocation overhead, along with greater use of explicit loops instead of optimized standard operations, in AI-generated code. These patterns give reviewers concrete problems to look for beyond whether the code appears to work. The authors report that feedback organized around the observed problem categories reduced targeted static-analysis warnings by 11.1% and improved computational efficiency. The evidence concerns one enterprise's production code from April 2025 through April 2026. Research abstract