An engineer is building a system to detect AI-generated code commits using Git signals like metadata and line-count changes.
The core challenge is that developers can erase metadata and large code additions are not always AI-generated.
The engineer is seeking real-world evidence on which signals actually work.
What happened
A developer is building a system to identify code commits generated by AI tools, using Git-level signals like commit metadata, lines-of-code changes, and file modification patterns—but is struggling with confidence and calibration of these signals.
Why it matters
As AI coding assistants become more common, organizations may need to track or flag AI-assisted contributions in their codebases. However, the technical challenge is substantial: developers can strip metadata, and patterns like large code additions are not inherently AI-generated, making reliable detection difficult.
What to watch
The engineer is explicitly asking the community whether Git/CI-level signals are genuinely useful for this task, and whether a probabilistic risk-scoring approach would work better than a binary AI-vs-human classification—suggesting this remains an open, unsolved problem.
Ask the AI about this article →
The post reflects a practical challenge in modern software development: as AI coding tools become integrated into developer workflows, organizations may wish to detect or track their use—but the technical signals are ambiguous. The engineer's approach of using Git-level signals (metadata, commit statistics) is logical, since that is where visibility exists after code is pushed. However, the core problem is that these signals are both lossy (metadata can be stripped) and polysemous (large additions are caused by both humans and AI). The engineer's explicit question about whether to use probabilistic risk-scoring rather than binary classification suggests they recognize that the problem may not have a clean categorical answer, and that thresholds themselves may need to be tuned per organization or context.
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