  Context: Deep learning has achieved remarkable progress in various domains.
However, like any software system, deep learning systems contain bugs, some of
which can have severe impacts, as evidenced by crashes involving autonomous
vehicles. Despite substantial advancements in deep learning techniques, little
research has focused on reproducing deep learning bugs, which is an essential
step for their resolution. Existing literature suggests that only 3% of deep
learning bugs are reproducible, underscoring the need for further research.
  Objective: This paper examines the reproducibility of deep learning bugs. We
identify edit actions and useful information that could improve the
reproducibility of deep learning bugs.
  Method: First, we construct a dataset of 668 deep-learning bugs from Stack
Overflow and GitHub across three frameworks and 22 architectures. Second, out
of the 668 bugs, we select 165 bugs using stratified sampling and attempt to
determine their reproducibility. While reproducing these bugs, we identify edit
actions and useful information for their reproduction. Third, we used the
Apriori algorithm to identify useful information and edit actions required to
reproduce specific types of bugs. Finally, we conducted a user study involving
22 developers to assess the effectiveness of our findings in real-life
settings.
  Results: We successfully reproduced 148 out of 165 bugs attempted. We
identified ten edit actions and five useful types of component information that
can help us reproduce the deep learning bugs. With the help of our findings,
the developers were able to reproduce 22.92% more bugs and reduce their
reproduction time by 24.35%.
  Conclusions: Our research addresses the critical issue of deep learning bug
reproducibility. Practitioners and researchers can leverage our findings to
improve deep learning bug reproducibility.
