Bootstrapping Mobile App Development. - Scott Barnett; Rajesh Vasa; John Grundy 001 (ICSE (2), 2015)

A multi-view framework for generating mobile apps. - Scott Barnett; Iman Avazpour; Rajesh Vasa; John C. Grundy (VL/HCC, 2015)

A Conceptual Model for Architecting Mobile Applications. - Scott Barnett; Rajesh Vasa; Antony Tang (WICSA, 2015)

Data Provenance for Sport. - Andrew J. Simmons; Scott Barnett; Simon Vajda; Rajesh Vasa (CoRR, 2018)

Supporting multi-view development for mobile applications. - Scott Barnett; Iman Avazpour; Rajesh Vasa; John Grundy 001 (J. Comput. Lang., 2019)

A large-scale comparative analysis of Coding Standard conformance in Open-Source Data Science projects. - Andrew J. Simmons; Scott Barnett; Jessica Rivera-Villicana; Akshat Bajaj; Rajesh Vasa (ESEM, 2020)

Interpreting cloud computer vision pain-points: a mining study of stack overflow. - Alex Cummaudo; Rajesh Vasa; Scott Barnett; John C. Grundy; Mohamed Abdelrazek 001 (ICSE, 2020)

Beware the evolving 'intelligent' web service! an integration architecture tactic to guard AI-first components. - Alex Cummaudo; Scott Barnett; Rajesh Vasa; John C. Grundy; Mohamed Abdelrazek 001 (ESEC/SIGSOFT FSE, 2020)

Threshy: supporting safe usage of intelligent web services. - Alex Cummaudo; Scott Barnett; Rajesh Vasa; John C. Grundy (ESEC/SIGSOFT FSE, 2020)

Ranking Computer Vision Service Issues using Emotion. - Maheswaree Kissoon Curumsing; Alex Cummaudo; Ulrike Maria Graetsch; Scott Barnett; Rajesh Vasa (CoRR, 2020)

Towards a taxonomy for annotation of data science experiment repositories. - Shangeetha Sivasothy; Scott Barnett; Niroshinie Fernando; Rajesh Vasa; Roopak Sinha; Andrew J. Simmons (SCAM, 2021)

Emotions in Computer Vision Service Q&A. - Alex Cummaudo; Ulrike Maria Graetsch; Maheswaree Kissoon Curumsing; Rajesh Vasa; Scott Barnett; John Grundy 001 (SEmotion@ICSE, 2021)

A Framework for Evaluating MRC Approaches with Unanswerable Questions. - Hung Du; Srikanth Thudumu; Sankhya Singh; Scott Barnett; Irini Logothetis; Rajesh Vasa; Kon Mouzakis (e-Science, 2022)

PiMS: A Pre-ML Labelling Tool. - Irini Logothetis; Scott Barnett; Leonard Hoon; Srikanth Thudumu; Joseph Mathew; Carl Luckhoff; Gerard O'Reilly; David Collard; Rajesh Vasa; Kon Mouzakis; Mark Fitzgerald (e-Science, 2022)

Subspace based Anomaly Detection Framework for Point Clouds. - Johnahan Van Zyl; Hung Du; Srikanth Thudumu; Irini Logothetis; Scott Barnett; Rajesh Vasa; Kon Mouzakis (e-Science, 2022)

MLSmellHound: A Context-Aware Code Analysis Tool. - Jai Kannan; Scott Barnett; Luis Cruz 002; Anj Simmons; Akash Agarwal (ICSE (NIER), 2022)

Towards Better mHealth Apps: Understanding Current Challenges and User Expectations. - Ben Joseph Philip; Mohamed Abdelrazek 001; Scott Barnett; Alessio Bonti; John C. Grundy (MOBILESoft, 2022)

Comparative analysis of real bugs in open-source Machine Learning projects - A Registered Report. - Tuan Dung Lai; Anj Simmons; Scott Barnett; Jean-Guy Schneider; Rajesh Vasa (CoRR, 2022)

Decentralized Federated Learning Strategy with Image Classification using ResNet Architecture. - Hung Du; Srikanth Thudumu; Sankhya Singh; Scott Barnett; Irini Logothetis; Rajesh Vasa; Kon Mouzakis (CCNC, 2023)

mHealthSwarm: A Unified Platform for mHealth Applications. - Ben Joseph Philip; Yasmeen Anjeer Alshehhi; Mohamed Abdelrazek 001; Scott Barnett; Alessio Bonti; John Grundy 001 (ENASE, 2023)

Cell2Doc: ML Pipeline for Generating Documentation in Computational Notebooks. - Tamal Mondal; Scott Barnett; Akash Lal; Jyothi Vedurada (ASE, 2023)

MLGuard: Defend Your Machine Learning Model! - Sheng Wong; Scott Barnett; Jessica Rivera-Villicana; Anj Simmons; Hala Abdelkader; Jean-Guy Schneider; Rajesh Vasa (SE4SafeML@SIGSOFT FSE, 2023)

Toward a Unified mHealth Platform: A Survey of Current User Challenges and Expectations. - Ben Joseph Philip; Mohamed Almorsy Abdelrazek; Scott Barnett; Alessio Bonti; John Grundy 001 (IEEE Access, 2023)

Green Runner: A tool for efficient model selection from model repositories. - Jai Kannan; Scott Barnett; Anj Simmons; Taylan Selvi; Luis Cruz 002 (CoRR, 2023)

Evaluating LLMs on Document-Based QA: Exact Answer Selection and Numerical Extraction using Cogtale dataset. - Zafaryab Rasool; Scott Barnett; Stefanus Kurniawan; Sherwin Balugo; Rajesh Vasa; Courtney Chesser; Alex Bahar-Fuchs (CoRR, 2023)

Estimating Patient-Level Uncertainty in Seizure Detection Using Group-Specific Out-of-Distribution Detection Technique. - Sheng Wong; Anj Simmons; Jessica Rivera-Villicana; Scott Barnett (Sensors, 2023)

ML-On-Rails: Safeguarding Machine Learning Models in Software Systems - A Case Study. - Hala Abdelkader; Mohamed Abdelrazek 001; Scott Barnett; Jean-Guy Schneider; Priya Rani; Rajesh Vasa (CAIN, 2024)

Seven Failure Points When Engineering a Retrieval Augmented Generation System. - Scott Barnett; Stefanus Kurniawan; Srikanth Thudumu; Zach Brannelly; Mohamed Abdelrazek 001 (CAIN, 2024)

Green Runner: A Tool for Efficient Deep Learning Component Selection. - Jai Kannan; Scott Barnett; Anj Simmons; Taylan Selvi; Luis Cruz 002 (CAIN, 2024)

LLMs for Test Input Generation for Semantic Applications. - Zafaryab Rasool; Scott Barnett; David Willie; Stefanus Kurniawan; Sherwin Balugo; Srikanth Thudumu; Mohamed Abdelrazek 001 (CAIN, 2024)

LLMs for Test Input Generation for Semantic Caches. - Zafaryab Rasool; Scott Barnett; David Willie; Stefanus Kurniawan; Sherwin Balugo; Srikanth Thudumu; Mohamed Almorsy Abdelrazek (CoRR, 2024)

Large language models for generating rules, yay or nay? - Shangeetha Sivasothy; Scott Barnett; Rena Logothetis; Mohamed Abdelrazek 001; Zafaryab Rasool; Srikanth Thudumu; Zach Brannelly (CoRR, 2024)

Fine-Tuning or Fine-Failing? Debunking Performance Myths in Large Language Models. - Scott Barnett; Zach Brannelly; Stefanus Kurniawan; Sheng Wong (CoRR, 2024)

Minimising changes to audit when updating decision trees. - Anj Simmons; Scott Barnett; Anupam Chaudhuri; Sankhya Singh; Shangeetha Sivasothy (CoRR, 2024)

RAGProbe: An Automated Approach for Evaluating RAG Applications. - Shangeetha Sivasothy; Scott Barnett; Stefanus Kurniawan; Zafaryab Rasool; Rajesh Vasa (CoRR, 2024)

Evaluating LLMs on document-based QA: Exact answer selection and numerical extraction using CogTale dataset. - Zafaryab Rasool; Stefanus Kurniawan; Sherwin Balugo; Scott Barnett; Rajesh Vasa; Courtney Chesser; Benjamin M. Hampstead; Sylvie Belleville; Kon Mouzakis; Alex Bahar-Fuchs (Nat. Lang. Process. J., 2024)

Comparative analysis of real issues in open-source machine learning projects. - Tuan Dung Lai; Anj Simmons; Scott Barnett; Jean-Guy Schneider; Rajesh Vasa (Empir. Softw. Eng., 2024)

RAGProbe: Breaking RAG Pipelines with Evaluation Scenarios. - Shangeetha Sivasothy; Scott Barnett; Stefanus Kurniawan; Zafaryab Rasool; Rajesh Vasa (CAIN, 2025)

Channel-annotated deep learning for enhanced interpretability in EEG-based seizure detection. - Sheng Wong; Anj Simmons; Jessica Rivera-Villicana; Scott Barnett; Shobi Sivathamboo; Piero Perucca; Zongyuan Ge; Patrick Kwan; Levin Kuhlmann; Terence J. O'Brien (Biomed. Signal Process. Control., 2025)

The M-factor: A Novel Metric for Evaluating Neural Architecture Search in Resource-Constrained Environments. - Srikanth Thudumu; Hy Nguyen; Hung Du; Nhat Duong; Zafaryab Rasool; Rena Logothetis; Scott Barnett; Rajesh Vasa; Kon Mouzakis (CoRR, 2025)

Monitoring Machine Learning Systems: A Multivocal Literature Review. - Hira Naveed; Scott Barnett; Chetan Arora 002; John C. Grundy; Hourieh Khalajzadeh; Omar Haggag (CoRR, 2025)