  It is a sad reflection of modern academia that code is often ignored after
publication -- there is no academic 'kudos' for bug fixes / maintenance. Code
is often unavailable or, if available, contains bugs, is incomplete, or relies
on out-of-date / unavailable libraries. This has a significant impact on
reproducibility and general scientific progress. Neural Architecture Search
(NAS) is no exception to this, with some prior work in reproducibility.
However, we argue that these do not consider long-term reproducibility issues.
We therefore propose a checklist for long-term NAS reproducibility. We evaluate
our checklist against common NAS approaches along with proposing how we can
retrospectively make these approaches more long-term reproducible.
