Studying the Practices of Testing Machine Learning Software in the Wild

1 code implementation • 19 Dec 2023 • Moses Openja, Foutse khomh, Armstrong Foundjem, Zhen Ming, Jiang, Mouna Abidi, Ahmed E. Hassan

Aims: To fill this gap, we perform the first fine-grained empirical study on ML testing practices in the wild, to identify the ML properties being tested, the followed testing strategies, and their implementation throughout the ML workflow.

Autonomous Driving Fairness

Quality Issues in Machine Learning Software Systems

1 code implementation • 26 Jun 2023 • Pierre-Olivier Côté, Amin Nikanjam, Rached Bouchoucha, Ilan Basta, Mouna Abidi, Foutse khomh

We validate the identified quality issues via a survey with ML practitioners.