Organization profile

Profile Information

The research within the Software and Systems Research Group covers a wide range of topics in Computer Science. Our areas of work include programming theories and semantics, formal specification and verification, specialised/advanced program logics, program analysis and refinement, interactive theorem proving, reversible computing, internet of things and cyber-physical systems.

The aim and vision of our research is to enable and support the construction of reliable, safe, and secure software and systems. For this, we apply mathematically-founded techniques to the specification, modelling, verification and validation of software and systems. We are also keen to work closely with industrial partners to support the design and implementation of intelligent and reliable software and systems via knowledge transfer partnerships or consultancy. Our research also has strong connection and involvement with Teesside University Grand Challenge themes such as Resilient and Secure Societies, Digital and Creative Economy.

Fingerprint The fingerprint is based on mining the text of the scientific documents related to the associated persons. Based on that an index of weighted terms is created, which defines the key subjects of research unit

Wireless sensor networks Engineering & Materials Science
Communication Engineering & Materials Science
Data storage equipment Engineering & Materials Science
Sensors Engineering & Materials Science
Body sensor networks Engineering & Materials Science
Optical burst switching Engineering & Materials Science
Middleware Engineering & Materials Science
Switching networks Engineering & Materials Science

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Research Output 2000 2019

ABAC Requirements Engineering for Database Applications

Longstaff, J. & He, M., 2019.

Research output: Contribution to conferencePaperResearchpeer-review

Open Access
Requirements engineering
Access control
13 Downloads (Pure)

Multi-scale stochastic organization-oriented coarse-graining exemplified on the human mitotic checkpoint

Henze, R., Mu, C., Puljiz, M., Kamaleson, N., Huwald, J., Haslegrave, J., di Fenizio, P. S., Parker, D., Good, C., Rowe, J. E., Ibrahim, B. & Dittrich, P., 7 Mar 2019, In : Nature Scientific Reports. 9, 1, 3902.

Research output: Contribution to journalArticleResearchpeer-review

Open Access

Self-Improving Generative Adversarial Reinforcement Learning

Liu, Y., Zeng, Y., Chen, Y. & Tang, J., 15 May 2019.

Research output: Contribution to conferencePaperResearchpeer-review