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.

翻译：在持续演进的深度学习领域，确保项目质量与可靠性仍是关键挑战。本研究调查了GitHub上深度学习项目的测试实践，量化了测试方法的采用情况，重点关注测试自动化程度、测试类型（如单元测试、集成测试与系统测试）、测试套件增长率，以及不同项目版本间测试实践的演进趋势。我们基于定量与定性标准，对精选的300个代码库样本进行了分析。本研究报告了开源社区中深度学习项目测试实践的普及现状与相关洞见。