Software Engineering
Research Laboratory

In the Software Engineering Research Laboratory (SERL) at AUT we undertake world-class research in behavioural software engineering and in the engineering of software for large distributed systems. The results of our research have been published in the field’s top venues and we work closely with industry to ensure our research is relevant and has the potential for impact.

OUR RESEARCH

Read about our latest research projects, publications and research partners.

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OUR PEOPLE

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RESOURCES

Find out about the concepts and methodologies behind our research.

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Featured
Jim Featured Interview 300
Subash Humagain Success
nidhi-gowdra
SERL-Logo-Feature
SERL-Logo-Feature
Featured
Jim Featured Interview 300

Jim featured in AgileIT Interview Series

Jim Buchan has recently been featured in AgileIT interview conducted by Luke Pivac as part of Agilists in Auckland series of blogs.

Subash Humagain Success

PhD defence success

Subash Humagain successfully defended his PhD thesis, titled “Intelligent Dynamic Route Optimization and Road Pre-emption System for On-road Emergency Services” on 16 June ’21. Subash was supervised by Roopak Sinha, Edmund Lai and Prakash Ranjitkar (Auckland University).

nidhi-gowdra

PhD defence success

Nidhi Gowdra successfully defended his PhD thesis, titled “Exploring entropy-based optimization methods to increase Neural Network model training effectiveness” on 3 June 2021. Nidhi was supervised by Roopak Sinha, Wei Qi Yan and Stephen MacDonell.

SERL-Logo-Feature

Research published in Elsevier’s Information Systems journal

Chandan Sharma’s research article, titled “Practical and Comprehensive Formalisms for Modeling Contemporary Graph Query Languages” co-authored with Roopak Sinha and Kenneth Johnson has been accepted in Elsevier’s Information Systems Journal (h-index 85).

SERL-Logo-Feature

Research published in Elsevier’s Pattern Recognition Journal

Nidhi Gowdra’s research article, titled “Mitigating severe over-parameterization in deep convolutional neural networks through forced feature abstraction and compression with an entropy-based heuristic” co-authored with Roopak Sinha, Stephen MacDonell and Wei Qi Yan, has been accepted at Elsevier’s Pattern Recognition Journal (h-index 210).

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