David Cutting
Cited by
Cited by
Towards a culture of low-carbon research for the 21 st Century
C Le Quéré, S Capstick, A Corner, D Cutting, M Johnson, A Minns, ...
Tyndall Centre for Climate Change Research, Working Paper 161, 1-25, 2015
Mining user reviews of COVID contact-tracing apps: An exploratory analysis of nine European apps
V Garousi, D Cutting, M Felderer
Journal of Systems and Software 184, 111136, 2022
An Extensible Benchmark and Tooling for Comparing Reverse Engineering Approaches
D Cutting, J Noppen
International Journal in Advances in Software 8 (1&2), 115-124, 2015
Computing degree apprenticeships: An opportunity to address gender imbalance in the IT sector?
S Smith, E Taylor-Smith, K Fabian, M Barr, T Berg, D Cutting, J Paterson, ...
2020 IEEE Frontiers in Education Conference (FIE), 1-8, 2020
Development of a digital lifestyle modification intervention for use after transient ischaemic attack or minor stroke: a person-based approach
N Heron, SR O’Connor, F Kee, DR Thompson, N Anderson, D Cutting, ...
International journal of environmental research and public health 18 (9), 4861, 2021
What do users think of COVID-19 contact-tracing apps? An analysis of eight European apps
V Garousi, D Cutting, M Felderer
IEEE Software, 2021
Association between community-based self-reported COVID-19 symptoms and social deprivation explored using symptom tracker apps: a repeated cross-sectional study in Northern Ireland
JM McKinley, D Cutting, N Anderson, C Graham, B Johnston, U Mueller, ...
BMJ open 11 (6), e048333, 2021
What do users think of the UK’s three COVID-19 contact-tracing apps? A comparative analysis
V Garousi, D Cutting
BMJ Health & Care Informatics 28 (1), 2021
Enhancing legacy software system analysis by combining behavioural and semantic information sources
D Cutting
University of East Anglia, 2016
Working With Reverse Engineering Output for Benchmarking and Further Use
D Cutting, J Noppen
The Ninth International Conference on Software Engineering Advances (ICSEA), 2014
Use of Wearable Technologies with Machine Learning to Understand University Student Learning Behaviours to Predict Students at Risk of Failing
A McGowan, P Hanna, D Greer, J Busch, N Anderson, M Collins, ...
International Conference on Human Interaction and Emerging Technologies, 325-331, 2019
Anonymous vs. Non-Anonymous Backchannels: The Good, The Bad and the Ugly
A McDowell, D Cutting, A Allen, P Sage
Higher Education Academy STEM Conference, 2019
Backchannel in Large Learner Cohorts-Does Anonymity Matter? A Comparative Study.
A McDowell, D Cutting, P Sage, A Allen, A McGowan
Education and Information Systems, Technologies and Applications, 2019
Promoting learner engagement: measuring and characterising learner engagement using a collaborative online learning tool
A McDowell, D Cutting, P Sage, A Allen, A McGowan
International Conference on Education and New Developments, Porto, Portugal, 2019
Engagement Contexts of Software Engineering Education Projects
M Watson, D Cutting
Proceedings of the 2022 Annual Conference of the European Association for …, 2022
Automated Ki-67 proliferation scoring from histopathology images using Mobile and Cloud technology
M McConnell, R Gault, S Craig, D Cutting, A Rainer, J James
Irish Machine Vision and Image Processing Conference, 2021
Beyond “Presenceism”: Monitoring student engagement in the new normal
D Cutting, A McDowell, E Weir, G Trombino
Irish Learning Technology Association Educational Technology Conference 2021, 2021
Community-based COVID-19 spatial analysis in Northern Ireland using smartphone, self-reported symptom data
J McKinley, D Cutting, N Anderson, C Graham, B Johnston, U Mueller, ...
Public Health Agency, 2020
Northern Ireland COVID-19 New Cases Prediction from KCL/ZOECOVID Symptom Checker App Data (Casehood Prediction Report 20)
D Cutting, N Anderson
Queen's University Belfast, 2020
NI COVID Casehood Prediction Report 15
D Cutting, N Anderson
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