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Software Engineering

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Computational methods based on real intelligence

Software engineering typically concerns the development, deployment, maintenance and evaluation of high-quality software over extended periods of time (e.g., decades), at scale (e.g., globally distributed) and by teams of teams of software engineers. 

The software engineering research theme is multidisciplinary, innovative and collaborative. We work with academia, industry, policy makers, the third sector and community groups. We contribute empirically, theoretically and methodologically to both understand and improve software engineering practice, and to improve the impact of software engineering on individuals, groups, organisations, society, the economy and the environment.


MAIN RESEARCH AREAS

  • Evidence based software engineering

    We conduct empirical studies of software engineering to provide credible evidence for decision making in software development, maintenance and deployment. We offer expertise in code analysis, participatory action research, multi-vocal literature reviews and case studies. 

  • Fog and Cloud Computing

    We study automated software engineering for fog computing, continuous integration of service-oriented systems, and web API/mashup development and maintenance.

  • Human-centric issues software engineering

    From digital health devices, social network platforms, to smart living infrastructures, software systems are primarily designed and built to address humans needs and ambitions. A wide variety of human-centric issues impact the design, development, and use of software systems, including the demographics (age, gender, education, incomes), abilities (physical and mental), and values (privacy, security, environmental care, equity, and autonomy) of software practitioners, end-users, and wider stakeholders. Our work centres on the investigation of how human-centric issues affect SE practice and we develop methods, tools, and techniques to explicitly consider and address these issues through the SE decision making process, from systems requirements analysis, to design, implementation, testing, validation, deployment, maintenance, retirement, and disposal. 

  • Legacy systems

    We study legacy systems to better understand their complexity, and so better manage the maintenance and evolution of such legacy systems, and also reduce or minimise technical debt. We study such systems through analysis of multiple information sources. The ultimate aim is to de-risk such systems through supported transformation to modern architectures and techniques. 

  • Requirements Management and Software Evolution

    Requirements changes during software development create risk to cost, schedule and quality but also represent an opportunity to add value. How can we predict and manage requirements change? We have looked at predicting requirements change from project and repository data and making use of expert judgement. 

  • Search Based Software Engineering (SBSE)

    How can we use machine-based search techniques to solve different optimisation problems from various software engineering areas? Faced with a huge search spaces and the difficulty of using a human-based search, often we can find the solution(s) in metaheuristic searches and evolutionary algorithms. We have used genetic algorithms, simulated annealing, taboo search and other techniques to solve problems in software release planning and in automated software refactoring. 

  • Software Engineering for Artificial Intelligence

    We develop, evaluate and deploy technologies and practices for safe, fair, and trustworthy AI. 

  • Software testing and quality assurance

    For more than a decade, we have performed industry-relevant, applied research on software testing and quality assurance. Most our projects are in joint collaboration with industry and with various academic/industrial collaborators in the UK, Europe and elsewhere in the world. All our projects result in technology transfer, scientific/industrial papers, and completion of several MSc / PhD theses. 

  • Team-based software innovation

    We study the human and technical aspects of team-based software innovation, including remote work. The includes innovation practices (e.g., design thinking and agile), ‘thinking styles’, the tools for online collaboration, and team dynamics. 

OUR IMPACT

StopCOVID NI app: Academics in the research theme reviewed the software processes and source code for the StopCOVID NI module and also supervised student projects to study the mobile application. 

Values in Computing: Our work helps to identify the tensions within and between software development teams, stakeholders and end-users. By doing so, our research may help to anticipate technical decisions that could lead to breaches of social norms and interfere with the very public good that it wishes to support.

Linden foods - Blockchain for Food Traceability - developing and implementing blockchain technology to provide traceability on meat products from customer back to producer

EventMap - Applying multi objective optimisation to resource scheduling - developing a system to optimally manage and organise large training systems.

BT - (July 2021 - June 2023)

Fusion Project with Irish Diagnostic Lab Services - Data Warehousing for Scientific Data - developing a data warehouse system to manage animal lab test results (April 2016-Oct 2017)

 

 

 

 

 

INDUSTRIAL COLLABORATIONS
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KEY STAFF CONTACT
Professor Austen Rainer

a.rainer@qub.ac.uk  

+44 (0)28 9097 6795 


STAFF

SOFTWARE ENGINEERING Role Interest Area
A Rainer Theme Lead Team-based software innovation a.rainer@qub.ac.uk
D Greer Senior Lecturer Empirical Software Engineering
V Garousi  Senior Lecturer Software Engineering
M Ferrario Senior Lecturer Human-centred computing
D Athanasopoulos Lecturer Software Design
Y Sun Lecturer Software Engineering for AI
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School of Electronics, Electrical Engineering and Computer Science

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