22 citations · 43 across the 13 of their papers we have counts for
14 papers
Monitoring Machine Learning Systems: A Multivocal Literature Review
Hira Naveed, Scott Barnett, Chetan Arora +3
Context: Dynamic production environments make it challenging to maintain reliable machine learning (ML) systems. Runtime issues, such as changes in data patterns or operating conte…
ML-On-Rails: Safeguarding Machine Learning Models in Software Systems A Case Study
Hala Abdelkader, Mohamed Abdelrazek, Scott Barnett +3
Machine learning (ML), especially with the emergence of large language models (LLMs), has significantly transformed various industries. However, the transition from ML model protot…
Seven Failure Points When Engineering a Retrieval Augmented Generation System
Scott Barnett, Stefanus Kurniawan, Srikanth Thudumu +2
Software engineers are increasingly adding semantic search capabilities to applications using a strategy known as Retrieval Augmented Generation (RAG). A RAG system involves findin…
Evaluating LLMs on Document-Based QA: Exact Answer Selection and Numerical Extraction using Cogtale dataset
Zafaryab Rasool, Stefanus Kurniawan, Sherwin Balugo +7
Document-based Question-Answering (QA) tasks are crucial for precise information retrieval. While some existing work focus on evaluating large language models performance on retrie…
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…
Green Runner: A tool for efficient model selection from model repositories
Jai Kannan, Scott Barnett, Anj Simmons +2
Deep learning models have become essential in software engineering, enabling intelligent features like image captioning and document generation. However, their popularity raises co…
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…
MLSmellHound: A Context-Aware Code Analysis Tool
Jai Kannan, Scott Barnett, Luís Cruz +2
Meeting the rise of industry demand to incorporate machine learning (ML) components into software systems requires interdisciplinary teams contributing to a shared code base. To ma…
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…
Ranking Computer Vision Service Issues using Emotion
Maheswaree K Curumsing, Alex Cummaudo, Ulrike Maria Graetsch +2
Software developers are increasingly using machine learning APIs to implement 'intelligent' features. Studies show that incorporating machine learning into an application increases…
Interpreting Cloud Computer Vision Pain-Points: A Mining Study of Stack Overflow
Alex Cummaudo, Rajesh Vasa, Scott Barnett +2
Intelligent services are becoming increasingly more pervasive; application developers want to leverage the latest advances in areas such as computer vision to provide new services…
Data Provenance for Sport
Andrew J. Simmons, Scott Barnett, Simon Vajda +1
Data analysts often discover irregularities in their underlying dataset, which need to be traced back to the original source and corrected. Standards for representing data provenan…