FAQ

When teams worry whether a submission was written by a human or generated by an LLM, a reliable code ai detector can be a lifesaver. At a glance, the landscape splits into classic code-plagiarism tools and newer ML detectors that try to spot AI “fingerprints.” Tools like MOSS remain useful for catching direct copying between student submissions and repositories — it compares code structure and highlights suspicious overlaps, which is still excellent for intra-class checks.

Newer services such as Codequiry explicitly advertise AI-code detection and web-scale scanning, which helps when students pull code from GitHub or when developers want to know if snippets were LLM-generated. Their strength is breadth — they look beyond pairwise comparisons to internet sources and stylistic patterns — but they can produce false positives when human code resembles model output.

On the research side, methods like DetectGPT show that statistical traces and probability curvature can help detect machine-generated text; these approaches are promising but not silver bullets once models change or are fine-tuned. That means academic detectors may lag real-world model advances.

General AI-content checkers (GPTZero, Copyleaks, etc.) can flag likely AI writing but were built mostly for prose; applying them to code requires caution because code has different structural signals and smaller context windows.

Finally, don’t forget defensive practices: combine automated detection with code reviews, test runs, and behavioral checks. Tools like Keploy — which generate and replay API tests and help automate test coverage — aren’t detectors per se, but they’re invaluable for exposing flaky or out-of-spec code (including suspicious AI snippets) during CI. Integrating detection with testing reduces risk more than relying on a single classifier.