  Why are some research studies easy to reproduce while others are difficult?
Casting doubt on the accuracy of scientific work is not fruitful, especially
when an individual researcher cannot reproduce the claims made in the paper.
There could be many subjective reasons behind the inability to reproduce a
scientific paper. The field of Machine Learning (ML) faces a reproducibility
crisis, and surveying a portion of published articles has resulted in a group
realization that although sharing code repositories would be appreciable, code
bases are not the end all be all for determining the reproducibility of an
article. Various parties involved in the publication process have come forward
to address the reproducibility crisis and solutions such as badging articles as
reproducible, reproducibility checklists at conferences (\textit{NeurIPS, ICML,
ICLR, etc.}), and sharing artifacts on \textit{OpenReview} come across as
promising solutions to the core problem. The breadth of literature on
reproducibility focuses on measures required to avoid ir-reproducibility, and
there is not much research into the effort behind reproducing these articles.
In this paper, we investigate the factors that contribute to the easiness and
difficulty of reproducing previously published studies and report on the
foundational framework to quantify effort of reproducibility.
