I am somewhat disappointed with the lack of replicability in the field of machine learning and computer science. I think there is not much excuse for releasing a ML paper on a new algorithm or modeling technique without a link to a source code repo. Sure, your research code may not be pretty, but that should not be a deal-breaker. I hope reviewers start rewarding papers with links to source code. This should also stimulate refactoring, documenting, and cleaning up the linked source code.
Also a standard unified process for replicability, reproducibility, and reuse is needed. Dock points for not stating random seeds, hardware used, metadata, etc.
I have tried and failed to reproduce some findings on ML papers. Sometimes graphs are being significantly smoothed or filtered which makes results look better, other times core components of algorithms are not described and the findings cannot be reproduced at all.
Source code, or at the very least proper pseudocode, should be mandatory for all published computer science research.
Also a standard unified process for replicability, reproducibility, and reuse is needed. Dock points for not stating random seeds, hardware used, metadata, etc.