20 citations · 40 across the 8 of their papers we have counts for
6 papers · 1 filter
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…
MLTEing Models: Negotiating, Evaluating, and Documenting Model and System Qualities
Katherine R. Maffey, Kyle Dotterrer, Jennifer Niemann +3
Many organizations seek to ensure that machine learning (ML) and artificial intelligence (AI) systems work as intended in production but currently do not have a cohesive methodolog…
Self-Adaptation in Industry: A Survey
Danny Weyns, Ilias Gerostathopoulos, Nadeem Abbas +13
Computing systems form the backbone of many areas in our society, from manufacturing to traffic control, healthcare, and financial systems. When software plays a vital role in the…
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…
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…
Characterizing and Detecting Mismatch in Machine-Learning-Enabled Systems
Grace A. Lewis, Stephany Bellomo, Ipek Ozkaya
Increasing availability of machine learning (ML) frameworks and tools, as well as their promise to improve solutions to data-driven decision problems, has resulted in popularity of…