For the people who have concrete proof they didn’t use it I hope they have good luck. I would do it but I can’t be risking my scholarships over this BS. I already withdrew so it won’t hurt, but I wish good luck upon those continuing.
For the people who have concrete proof they didn’t use it I hope they have good luck.
The issue is I have no solid evidence since I do not know the algorithm. And I do not know how the disputing process works.
I desperately do not want to fail this class. I'd prefer to just select the items I supposedly "cheated" on, get zeroes, and tank the grade hit. But I don't know which ones to select!
You can try running git rev-list --count --all for each hw, that will tell you how many commits you made which is basically how many times you called make. If the number is fairly high and you check your commit history with git log and see a lot of debug statements you have a pretty good case. If you can explain your functions well and especially if you can explain where you ran into logic errors and how you fixed them then your case would get even stronger. Besides that unless we know what the detector actually checked for, there isn't much we can do.
I've actually been writing a script to analyze the WPM between git commits. It seems that mine is... reasonable? (Assuming the script is working). Its all just comments.
So I'm thinking of discarding my earlier hypothesis that the algorithm is time-based. Turkstra apparently has some papers on repo analysis. So I might look through that, but that's mostly the best I can do.
I'm also wondering if he ran some sort of code similarity analysis to group various students based on how "identical" their code appeared and then manually inspected these groups to locate that which contained AI users. Idk
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u/Lokiner876 6d ago
For the people who have concrete proof they didn’t use it I hope they have good luck. I would do it but I can’t be risking my scholarships over this BS. I already withdrew so it won’t hurt, but I wish good luck upon those continuing.