AI x Software Engineering & Testing (ASET)
The ASET Research Group is one of the largest groups of its kind in the UK, developing innovative approaches to software testing and quality assurance.
We develop and evaluate practical software engineering techniques to support the efficient development of robust, maintainable software and cyber-physical systems. Much of our research is geared towards the growing role of AI in the software-development lifecycle, from agentic software development through to systems such as autonomous vehicles that themselves incorporate AI into their core functionality.
Research themes
- AI
AI is a theme that cuts across all of our research themes. We are interested in the development of new AI-enabled technologies to support and enhance traditional software engineering tasks (with a particular expertise in testing) – as elaborated in our Test Generation and Test Analytics and Test Validation themes. We also have an established track-record of developing novel techniques to test newer classes of AI-enabled systems, from self-driving cars to smart manufacturing systems, as elaborated in our “Hard-to-test” systems theme.
- Test Generation
Test Generation is concerned with the efficient identification of inputs that will expose a bug. Our group has developed approaches to cater for a wide range of testing scenarios - from white-box systems where code and runtime-state can be monitored through to black-box systems where we can only control inputs and observe outputs. We have developed techniques that incorporate search-based algorithms (McMinn, Rojas, Shin), Model-Based Testing (Bogdanov, Derrick, Hierons, Walkinshaw), Fuzzing (Walkinshaw), Exploratory Testing techniques (McMinn, Walkinshaw), and LLM-based approaches (McMinn, Shin, Walkinshaw).
- Test Analytics and Test Validation
Test Analytics and Test Validation are concerned with the assessment of existing test-sets to establish their capacity to reliably expose any bugs that might exist in the system. From a test-analytics standpoint we have developed approaches to detect test-flakiness, where tests inconsistently pass or fail from one run to another (McMinn). We have a longstanding interest in the assessment of test sets in terms of their adequacy. Much of our work has focussed on Mutation Testing (Hierons, McMinn, Shin), with a recent focus on testing Rust programs. We also have a longstanding interest in the use of Machine Learning and, more recently, Causal Inference techniques to reason about the behaviour of systems under test when there are limited specifications to draw upon (Bogdanov, Derrick, Hierons, Shin, Walkinshaw).
- "Hard-to-test" Systems
“Hard-to-test” systems refers to a variety of classes of system that do not fit into the traditional mould of a software system, and therefore present their own additional testing challenges, which may include long run-times, non-determinism, and environmental dependencies that are hard to control. We have recently focussed on Augmented / Extended Reality systems (Rojas), Autonomous Driving Systems (Shin, Walkinshaw), Robotic systems and other cyber-physical systems (Bogdanov, Hierons, Rojas, Walkinshaw) - much of this work has been in collaboration with colleagues at the Advanced Manufacturing Research Centre (AMRC).
Core members
Academic staff
- (head of group)
- Dr José Miguel Rojas
Research staff
PhD students
- Harry J Bolton
- Rimsha Chaudhry
- Joel Hogg
- Zalán B Lévai
- Guannan Lou
- Giulia Romana Neri
- Olek Osikowicz
- Nathan Shaw
- Mark W Winteringham
- Affiliated academics
- ( University of Passau)
Publications
- Academic articles
Here you can find research publications for the AI x Software Engineering & Testing Research Group, listed by academic. The head link navigates to the official web page for the relevant academic (with highlighted favourite publications). The remaining links navigate to their DBLP author page, their Google Scholar citations page and optionally a self-maintained publications page.
Academic staff
Dr Donghwan Shin Affiliated academics
- Research theses
Here you can find recently-published PhD (and MPhil) theses, which have been deposited in the repository. Follow links to the abstract, and then to the full thesis (if public) or to a request form (if a time-embargo restriction has been placed on public release).
Recently published theses
Dr Islam Elgendy 2025 Dr Richard Somers 2025 Dr Muhammad Firhard Roslan 2025 Dr Owain Parry 2023 Dr Andrew Graham Clark 2023 Dr Ibrahim Althomali 2022 Dr Benjamin Simon Clegg 2021 Dr Qamar Naith 2021 Dr Eidah Alzahrani 2021 Dr Nasser Albunian 2020 Dr Abdullah Alsharif 2020 Dr Michael Foster 2020 Dr Thomas Weripuo Gyeera 2019 Dr Michael Herzberg 2019 Dr David Paterson 2019 Dr Thomas White 2019 Dr Michal Soucha 2019 Dr Sadeen Alharbi 2018 Dr Krenare Pireva 2018 Dr Thomas Walsh 2018 Dr Ermira Daka 2018 Dr Shtwai Alsubai 2018 Dr José Carlos Medeiros de Campos 2017 Dr Hanaa Al Zadjali 2017 Dr Dimitrios Kourtesis 2017 Dr Maria Ulfah Siregar 2016 Mr Samer Al Khazraji 2016 Dr Sina Shamshiri 2016 Mr Konstantinos Rousis 2016 Dr Abdullah Alsaeedi 2016 Dr Rustem Dautov 2016 Dr Fotios Gonidis 2016 Dr Christopher Wright 2016 Dr Alaa Almelibari 2015 Dr Ahmad Subahi 2015 Dr Othlapile Dinakenyane 2014 Ms Isidora Petreska 2014 Dr Joseph Vella 2014 Dr Andrea Corbett 2013 Dr Sheeva Afshan 2013 Dr Norah Farooqi 2013 Dr Mathew Hall 2013 Dr Ognen Paunovski 2013 Dr Ervin Ramollari 2013