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.
Ali, Q., Riganelli, O., Mariani, L. (2024). Testing in the Evolving World of DL Systems: Insights from Python GitHub Projects. In IEEE International Conference on Software Quality, Reliability and Security, QRS (pp.25-35). Institute of Electrical and Electronics Engineers Inc. [10.1109/QRS62785.2024.00013].
Testing in the Evolving World of DL Systems: Insights from Python GitHub Projects
Ali Q.;Riganelli O.;Mariani L.
2024
Abstract
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.
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
