5 citations · 8 across the 5 of their papers we have counts for
5 papers
Quality Model for Machine Learning Components
Grace A. Lewis, Rachel Brower-Sinning, Robert Edman +5
Despite increased adoption and advances in machine learning (ML), there are studies showing that many ML prototypes do not reach the production stage and that testing is still larg…
Using Quality Attribute Scenarios for ML Model Test Case Generation
Rachel Brower-Sinning, Grace A. Lewis, Sebastían Echeverría +1
Testing of machine learning (ML) models is a known challenge identified by researchers and practitioners alike. Unfortunately, current practice for ML model testing prioritizes tes…
Beyond Testers' Biases: Guiding Model Testing with Knowledge Bases using LLMs
Chenyang Yang, Rishabh Rustogi, Rachel Brower-Sinning +3
Current model testing work has mostly focused on creating test cases. Identifying what to test is a step that is largely ignored and poorly supported. We propose Weaver, an interac…
Capabilities for Better ML Engineering
Chenyang Yang, Rachel Brower-Sinning, Grace A. Lewis +2
In spite of machine learning's rapid growth, its engineering support is scattered in many forms, and tends to favor certain engineering stages, stakeholders, and evaluation prefere…
Data Leakage in Notebooks: Static Detection and Better Processes
Chenyang Yang, Rachel A Brower-Sinning, Grace A. Lewis +1
Data science pipelines to train and evaluate models with machine learning may contain bugs just like any other code. Leakage between training and test data can lead to overestimati…