  In the ever-evolving field of Deep Learning (DL), ensuring project quality
and reliability remains a crucial challenge. This research investigates testing
practices within DL projects in GitHub. It quantifies the adoption of testing
methodologies, focusing on aspects like test automation, the types of tests
(e.g., unit, integration, and system), test suite growth rate, and evolution of
testing practices across different project versions. We analyze a subset of 300
carefully selected repositories based on quantitative and qualitative criteria.
This study reports insights on the prevalence of testing practices in DL
projects within the open-source community.
