Cheating prevention and detection

On Coderbyte, we have deployed both industry-standard as well as proprietary methodologies to prevent and detect cheating.

Cheating prevention

To reduce the ability for candidates to cheat, we offer the following capabilities:

Cheating detection

If a candidate cheated, we provide several automated and manual detection methodologies. The following are how we display automated cheating detection:

  • If a candidate copy and pastes code into the code editor
  • If a candidate leaves the code editor tab during an assessment (the length of time impacts our detection algorithm)
  • If a candidate plagiarized from other candidates or from ChatGPT

Based on a combination of these factors, you will see a summarized cheating detection indicator within any Assessment's dashboard:

  • Not detected means that the candidate did not behave in any ways as to indicate that they cheated
  • Likely means that the candidate behaved in ways that indicate they likely cheated
  • Detected means that the candidate behaved in ways that indicate they definitely cheated

On the individual candidate report, you can see the overview of cheating that was detected:

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We also provide the following manual cheating detection methodologies:

  • If you have Google search enabled within the code editor, you can see what each candidate Google searched for and clicked by clicking Expand all above the Challenge solutions section
  • Watch a video playback of all coding activities to see any suspicious behaviors
  • Compare the country of the person taking the Assessment with the expected country of the candidate
  • Identify candidates and ask them to explain answers over webcam video
  • Enable webcam verification and proctoring to ensure that (i) candidates are who they say they are and (ii) that candidates worked on their solution independently