31 citations · 38 across the 5 of their papers we have counts for
5 papers
The Product Beyond the Model -- An Empirical Study of Repositories of Open-Source ML Products
Nadia Nahar, Haoran Zhang, Grace Lewis +2
Machine learning (ML) components are increasingly incorporated into software products for end-users, but developers face challenges in transitioning from ML prototypes to products.…
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…
Aspirations and Practice of Model Documentation: Moving the Needle with Nudging and Traceability
Avinash Bhat, Austin Coursey, Grace Hu +5
The documentation practice for machine-learned (ML) models often falls short of established practices for traditional software, which impedes model accountability and inadvertently…
On Debugging the Performance of Configurable Software Systems: Developer Needs and Tailored Tool Support
Miguel Velez, Pooyan Jamshidi, Norbert Siegmund +2
Determining whether a configurable software system has a performance bug or it was misconfigured is often challenging. While there are numerous debugging techniques that can suppor…
White-Box Analysis over Machine Learning: Modeling Performance of Configurable Systems
Miguel Velez, Pooyan Jamshidi, Norbert Siegmund +2
Performance-influence models can help stakeholders understand how and where configuration options and their interactions influence the performance of a system. With this understand…