Student manually reviews 102 F-Droid apps for AI-generated code using repo cues

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A student analyzed all 102 apps in F-Droid's September 12, 2026 update batch, manually classifying each by likelihood of being LLM-written using visual and contextual repo signals such as commit tone, README emoji density, and presence of AI agent infrastructure. No automated detection tools were used, and the author openly acknowledged the method's limitations, calling the tiers loose and the ratings superficial. The exercise highlighted that reliably detecting AI-authored code is not feasible even with full repository access and history. A key takeaway is that basing code review on AI-origin detection is a flawed approach, since provenance cannot be confirmed. Reviewers are instead urged to focus on the code itself — evaluating correctness, security, and logic regardless of whether a human or an AI wrote it.

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ProgrammingDEV Community ·

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