22 citations · 35 across the 9 of their papers we have counts for
11 papers · 1 filter
MLGuard: Defend Your Machine Learning Model!
Sheng Wong, Scott Barnett, Jessica Rivera-Villicana +4
Machine Learning (ML) is used in critical highly regulated and high-stakes fields such as finance, medicine, and transportation. The correctness of these ML applications is importa…
Comparative analysis of real bugs in open-source Machine Learning projects -- A Registered Report
Tuan Dung Lai, Anj Simmons, Scott Barnett +2
Background: Machine Learning (ML) systems rely on data to make predictions, the systems have many added components compared to traditional software systems such as the data process…
Requirements of API Documentation: A Case Study into Computer Vision Services
Alex Cummaudo, Rajesh Vasa, John Grundy +1
Using cloud-based computer vision services is gaining traction, where developers access AI-powered components through familiar RESTful APIs, not needing to orchestrate large traini…
Threshy: Supporting Safe Usage of Intelligent Web Services
Alex Cummaudo, Scott Barnett, Rajesh Vasa +1
Increased popularity of `intelligent' web services provides end-users with machine-learnt functionality at little effort to developers. However, these services require a decision t…
A large-scale comparative analysis of Coding Standard conformance in Open-Source Data Science projects
Andrew J. Simmons, Scott Barnett, Jessica Rivera-Villicana +2
Background: Meeting the growing industry demand for Data Science requires cross-disciplinary teams that can translate machine learning research into production-ready code. Software…
Beware the evolving 'intelligent' web service! An integration architecture tactic to guard AI-first components
Alex Cummaudo, Scott Barnett, Rajesh Vasa +2
Intelligent services provide the power of AI to developers via simple RESTful API endpoints, abstracting away many complexities of machine learning. However, most of these intellig…