TERA: optimizing stochastic regression tests in machine learning projects
Saikat Dutta, Jeeva Selvam, Aryaman Jain, Saša Misailovíc · 2021
The stochastic nature of many Machine Learning (ML) algorithms makes testing of ML tools and libraries challenging. ML algorithms allow a developer to control their accuracy and run-time through a set of hyper-parameters, which are typically manually selected in tests. This choice is often too conservative and leads to slow test executions, thereby increasing the cost of regression testing.