Applied Software Engineering and Data Analytics Group (ASEDA)
Source: https://www.brookes.ac.uk/research/units/tde/groups/applied-software-engineering-and-data-analytics Parent: https://www.brookes.ac.uk/engage-and-innovate/consultancy
Group Leader(s): Professor Rachel Harrison
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About us
Research impact
Membership
Projects
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About us Research impact Membership Projects
About us
The Applied Software Engineering and Data Analytics Research Group takes an empirical and experimental approach to software engineering, studying software systems in order to characterise and improve them. The Group provides a forum for researchers to exchange and discuss the latest innovative Software Engineering (SE) techniques and practices. We are also interested in AI solutions to SE problems and vice versa.
Our data analytics work focuses on data collection, analysis, summarisation and classification. For much of this research we depend on our knowledge of machine learning and big data.
The Group also works on cloud computing, big data, web technologies and network softwarisation. More specifically, the work focuses on: cloud services selection; big data management in NoSQL and the cloud; network softwarisation in cloud and IoT environments, web services and service-oriented computing.
In addition, the Group’s research involves aspects of eHealth using artificial intelligence, with a strong focus on usability and data visualisation. There is particular interest in technology for people living with diabetes.
Part of
- School of Engineering, Computing and Mathematics
- Artificial Intelligence, Data Analysis and Systems (AIDAS) Institute
Related courses
- Computing (MPhil / PhD / Masters by Research / PhD by Published Work)
- Computer Science (BSc (Hons))
- Data Science and Artificial Intelligence (MSc)
Research impact
The research of the Applied Software Engineering and Data Analytics Group involves a diverse collection of international stakeholders, in alignment with the faculty’s impact policy for computing that prioritises world-leading research with global impact. Some of the particular areas of impact are as follows:
- Related industries: We work to strengthen the competitiveness and growth of companies by establishing collaborations with mutual benefit to solve problems affect society more broadly.
- Healthcare: Our work has shown significant benefit to large cohorts of patients across the globe by improving the use of adaptive techniques and facilitating patient engagement in healthcare. The need for digital tools to manage healthcare has become even more evident during the Covid-19 pandemic.
- Community: We regularly engage with the general public, through newsletters, press releases, videos and social media, which have garnered considerable interest.
- Global research community: Our prize-winning research and our knowledge exchange events attract interest from a broad selection of disciplines interested in technological solutions to everyday problems.
- Pedagogy: Our research is embedded into the curriculum to promote student engagement with nationally recognised results.
Membership
Staff members
- Staff
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Staff
| Name | Role | |
|---|---|---|
| Dr Arantza Aldea | Associate Professor | aaldea@brookes.ac.uk |
| Dr Kashinath Basu | Senior Lecturer in Computer Science | kbasu@brookes.ac.uk |
| Professor Rachel Harrison | Professor in Computer Science | rachel.harrison@brookes.ac.uk |
| Dr Samia Kamal | Associate Professor of Computer Science Education and Enterprise | skamal@brookes.ac.uk |
| Dr Clare Martin | Associate Professor | cemartin@brookes.ac.uk |
| Professor Muhammad Younas | Professor of Computer Science | m.younas@brookes.ac.uk |
Projects
Completed projects
- Completed projects
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Completed projects
| Project title and description | Investigator(s) | Funder(s) | Dates |
|---|---|---|---|
| Cloud services selection This project improves cloud service selection and provision by taking into account the characteristics of cloud services, representations of cloud services and their capabilities, users’ knowledge and service level agreements. | Professor Muhammad Younas | From: January 2020 Until: December 2021 | |
| Network softwarization in cloud and IoT Development of a multi-layer architecture for WoT: At the software layer, cloud and web technologies are used in order to represent and manage things at the higher levels of abstraction. At the network layer, SDN and NFV technologies are used for softwarisation of network infrastructure and functions. This helps to create virtual network services to support the QoS requirements of WoT applications. | Dr Kashinath Basu | From: January 2020 Until: December 2021 | |
| Big data management in NoSQL and cloud Development of a new transaction model which provides NoSQL systems with standard transaction support and stronger data consistency for NoSQL and cloud systems. | Professor Muhammad Younas | From: January 2020 Until: December 2021 | |
| Web Services and SOA Designed and developed new models and architectures for orchestration and composition of web services and their application in various domains such as cloud services, E-commerce and mobile applications. | Professor Muhammad Younas | From: January 2020 Until: December 2020 | |
| Patient Empowerment through Predictive PERsonalised decision support (PEPPER) The PEPPER project brought together computer scientists, clinicians and industry leaders to create a personalised decision support system for diabetes management. It was a major European project in collaboration with Imperial College London, University de Girona, Girona Biomedical Research Institute, Romsoft SRL and Cellnovo Ltd. | Dr Clare Martin | Horizon 2020 | From: January 2016 Until: March 2020 |
| Spectra-based fault localisation (Spectra) This project applied metaheuristics to solve the fault localisation problem. | Professor Rachel Harrison | From: January 2018 Until: December 2020 | |
| Multi-Criteria Decision Support using AI (MuD) Provided support for multi-criteria decision making by developing CBR and BBN systems. | Professor Rachel Harrison | From: January 2020 Until: December 2020 | |
| Automated review classification (ReClass) Found a solution to the problem of automatic review classification and analysis for online app reviews. | Professor Rachel Harrison | From: January 2020 Until: December 2020 | |
| AI apps for the mining of big data (AIMi) Provided support for the mining of big data by specifying, designing and implementing an app. This app pre-processed data, applied a semi-supervised classification algorithm and reported the results. | Professor Rachel Harrison | From: January 2019 Until: December 2020 | |
| Software Quality Improvement (SEQUIN) Improved the prediction of software defects so that managers and developers can be informed about which modules are likely to be defective and consequently require extra resources during development and maintenance. | Professor Rachel Harrison | From: January 2019 Until: December 2020 |
Software Quality Journal
Visit the Software Quality Journal
The Software Quality Journal addresses all aspects of software quality from both a practical and an academic viewpoint. It invites contributions from practitioners and academics, as well as national and international policy and standard making bodies, and sets out to be the definitive international reference source for such information.
Prof. Rachel Harrison serves as the Editor-in-Chief of the journal.