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.

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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

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 Elgendy2025
Dr Richard Somers2025
Dr Muhammad Firhard Roslan2025
Dr Owain Parry2023
Dr Andrew Graham Clark2023
Dr Ibrahim Althomali2022
Dr Benjamin Simon Clegg2021
Dr Qamar Naith2021
Dr Eidah Alzahrani2021
Dr Nasser Albunian2020
Dr Abdullah Alsharif2020
Dr Michael Foster2020
Dr Thomas Weripuo Gyeera2019
Dr Michael Herzberg2019
Dr David Paterson2019
Dr Thomas White2019
Dr Michal Soucha2019
Dr Sadeen Alharbi2018
Dr Krenare Pireva2018
Dr Thomas Walsh2018
Dr Ermira Daka2018
Dr Shtwai Alsubai2018
Dr José Carlos Medeiros de Campos2017
Dr Hanaa Al Zadjali2017
Dr Dimitrios Kourtesis2017
Dr Maria Ulfah Siregar2016
Mr Samer Al Khazraji2016
Dr Sina Shamshiri2016
Mr Konstantinos Rousis2016
Dr Abdullah Alsaeedi2016
Dr Rustem Dautov2016
Dr Fotios Gonidis2016
Dr Christopher Wright2016
Dr Alaa Almelibari2015
Dr Ahmad Subahi2015
Dr Othlapile Dinakenyane2014
Ms Isidora Petreska2014
Dr Joseph Vella2014
Dr Andrea Corbett2013
Dr Sheeva Afshan2013
Dr Norah Farooqi2013
Dr Mathew Hall2013
Dr Ognen Paunovski2013
Dr Ervin Ramollari2013

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