For the past week I have been frustrated by an opensource code of a deep learning paper. This type of things are so common in academia. The particular code I looked at has missing documentation, hardcoded local paths, broken dataset download links and broken pretrained model download links. I have to fix bugs before the code can run. I'm very curious how did the author run that code with the bugs. I call them insince…
This sort of thing is aggravating to read. Frankly it comes off as really entitled. As researchers, the expectation is now that we not only have to do the research and write a paper like the good old days, but we have to release the code too. Okay, fine. But now that's not enough either -- the code has to be well-documented and clean. Ugh, alright, fine -- it's going to take me a few extra weeks of not doing research…
I can understand it may be part of a meaningful personal journey for you, and I appreciate that. But if no one else can validate your research they're correct to discredit it and you.
So what is the optimal outcome here? Should we hold you to a standard of reproducibility even if it is as minimal as, "actually describe your algorithms correctly and don't misrepresent a piece of code and a paper?" Or should everyone just decide you can find your own research funding if it's not going to help anyone?