The institute's expertise in software engineering encompasses the entire development lifecycle, and we focus on the approaches and tools to improve the engineering process of both emerging and widely used software systems.
Increasing the productivity and improving the quality of software remains a challenge for the software industry. Software reuse is one of the most promising approaches which, while simple in concept, poses many challenges in practice.
Our research and development uses semi-automatic adaptation of software components. We have developed new approaches to component adaptation using aspect-oriented software engineering techniques. We are also developing semantic-based approaches for describing components to allow developers to effectively locate the most relevant components for reuse.
We have actively extended our research on software reuse and evolution into the emerging areas of context-aware pervasive services, service-oriented systems, and cloud computing, which is the current focus of our research.
With the advent of multi-core computing, we are also looking at ways developers could exploit concurrent and parallel applications. These include the engineering of distributed parallel frameworks and provable and dependable concurrent systems.
Our work in advanced software engineering has enabled us to build wide links with industry and research institutions, and to publish widely in international journals and at conferences. The proven commercial value of our research in software reuse has resulted in the launch of a spin-out company, FlexiCAGE Ltd.
Smith, S. (2012). An Approach to Domain-based Scalable Context Management Architecture in Pervasive Environments. Personal and Ubiquitous Computing, 16, (6), 741-755.
Kerridge, J. (2008). Generative Aspect-Oriented Component Adaptation. IET Software, 2, (2), 149-160.
Sputh, B. (2010). Alting barriers: synchronisation with choice in Java using JCSP. Concurrency and Computation: Practice and Experience, 22, (8), 1049-1062.
Smith, S. (2010). A MDD-based Generic Framework for Context-aware Deeply Adaptive Service-based Processes. In: (Ed.) Proceedings of The 8th IEEE International Conference on Web Services (ICWS’10), , () ( ed.). (pp. ). Miami, USA: . IEEE Computer Society.
Smith, S. (2011). Recognize contextual situation in pervasive environments using process mining techniques. Journal OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING, 2, (1), 53-69.
Liu, X. (2012). Software Reuse in the Emerging Cloud Computing Era ( ed.). Pennsylvania, USA: . IGI Global Publishing.