Assessing Quality Attributes of Deep Learning Applications
Deep learning has revolutionized many applications, one of which concerns with combatting combined sewer overflows (CSOs). CSOs represent significant risks to human health as untreated water is discharged to the environment. However, for municipalities that have collected large amounts of time-series data, assessing quality attributes (e.g., robustness) of deep learning solutions (e.g., RNN implementations of LSTM, GRU, and IndRNN) is challenging. In this research project, the Protege student will join a team of graduate students in the Software Engineering Research Lab to develop novel and scalable ways to automatically test deep learning solution’s quality aspects in the context of CSOs.
Assoc Professor, CEAS - Computing Sciences & Informatics
832 Rhodes Hall