31 citations · 68 across the 6 of their papers we have counts for
5 papers · 1 filter
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.…
A Meta-Summary of Challenges in Building Products with ML Components -- Collecting Experiences from 4758+ Practitioners
Nadia Nahar, Haoran Zhang, Grace Lewis +2
Incorporating machine learning (ML) components into software products raises new software-engineering challenges and exacerbates existing challenges. Many researchers have invested…
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…
Collaboration Challenges in Building ML-Enabled Systems: Communication, Documentation, Engineering, and Process
Nadia Nahar, Shurui Zhou, Grace Lewis +1
The introduction of machine learning (ML) components in software projects has created the need for software engineers to collaborate with data scientists and other specialists. Whi…
An Exploratory Study to Find Motives Behind Cross-platform Forks from Software Heritage Dataset
Avijit Bhattacharjee, Sristy Sumana Nath, Shurui Zhou +4
The fork-based development mechanism provides the flexibility and the unified processes for software teams to collaborate easily in a distributed setting without too much coordinat…