There is a growing demand that future networks should be ubiquitous, pervasive and multimedia-capable, i.e. anyone should have broadband access at anytime and anywhere. This demands that the future network access should be broadband, high-speed, and with Quality of Service (QoS) support for various multimedia applications. This vision will not become true without the development of next-generation QoS-enabled broadband wireless access technologies given that the current access technologies (e.g. WiFi, GSM/GPRS/3G UMTS and DSL cable access) cannot sufficiently satisfy the above requirements. Orthogonal Frequency Division Multiple Access (OFDMA) technology has emerged as a most promising transmission technique candidate to be utilized for the next-generation broadband wireless access networks. Compared to conventional single-carrier systems, the orthogonal multi-carrier transmission scheme offers increased robustness to mitigate wireless multi-path distortion effects, and any subsets of the available subcarriers can be flexibly assigned to any users according to their specific QoS requirements.This proposal seeks to explore effective solutions for managing radio and transmission power resources to guarantee the QoS performance as perceived by users with minimum transmission power consumption for emerging OFDMA-based broadband wireless access systems. We plan to design a cross-layer optimization scheme, where subcarrier and power resources are optimally allocated by jointly considering the information from both physical and upper layers. Information theory and advanced queuing theory will be combined together for modelling wireless system dynamics. Specifically, a novel channel estimation method is to be investigated for physical layer to estimate channel state information, and then to be utilized to formulate a robust power bit loading model. An advanced Markov Modulated Poisson Process (MMPP) queuing model is to be created for modelling QoS performance of upper layer heterogeneous multimedia applications. At the scheduler, a cross-layer multi-objective optimization will then be formulated and low-complexity algorithms are sought to search optimal solutions of joint subcarrier and power allocation. The outcomes of this project will make a significant contribution toward acceleration of the rapid and ubiquitous deployment of emerging next-generation broadband wireless access systems in the UK and worldwide.

Humans find it hard to develop systems that balance many competing and conflicting operational objectives. Even meeting a single objective requires automated support. For example, there has been a long and rich history of research into techniques to optimise compiled code size and speed. Unfortunately, speed and size are but two of many objectives that the next generation of software systems will have to meet. Emergent computing application paradigms require systems that are not only reliable, compact and fast, but also which optimise many different competing and conflicting objectives such as response time, throughput and consumption of resources. Humans cannot be expected to optimally balance these multiple competing constraints and may miss potentially valuable solutions. Techniques are therefore required that can either automatically create code that balances many conflicting objectives or that can provide support to the human who seeks to do so. The GISMO project seeks to do both. It will develop automated techniques to produce new versions of components of existing systems that meet newly defined objectives. After a period of running the old and new component in parallel, the programmer may decide to adopt the newly evolved component. However, the beauty of the GISMO approach is that it does not insist that the programmer must accept the evolved solution in order to be useful. The programmer can also use GISMO to explore the multi-objective candidate solution space, gaining insight into what can be achieved by balancing several competing constraints.

Testing involves examining the behaviour of a system in order to discover potential faults. The problem of determining the desired correct behaviour for a given input is called the Oracle Problem. Since manual testing is expensive and time consuming there has been a great deal of work on automation and part automation of Software Testing. Unfortunately, it is often impossible to fully automate the process of determining whether the system behaves correctly. This must be performed by a human, and the cost of the effort expended is referred to as the Human Oracle Cost.RE-COST will develop Search-Based Optimisation techniques to attack the Human Oracle Cost problem quantitatively and qualitatively. The quantitative approach will develop methods and algorithms to both reduce the number of test cases and the evaluation effort per test case. The qualitative approach will develop methods and algorithms that will reduce test case cognition time.The RE-COST project seeks to transform the way that researchers and practitioners think about the problem of Software Test Data Generation. This has the potential to provide a breakthrough in Software Testing, dramatically increasing real world industrial uptake of automated techniques for Software Test Data Generation.

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

Current software development processes are expensive, laborious and error prone. They achieve adaptivity at only a glacial pace, largely through enormous human effort, forcing highly skilled engineers to waste significant time adapting many tedious implementation details. Often, the resulting software is equally inflexible, forcing users to also rely on their innate human adaptivity to find "workarounds". As the letters of support from the DAASE industrial partners demonstrate, this creates a pressing need for greater automation and adaptivity. Suppose we automate large parts of the development process using computational search. Requirements engineering, project planning and testing now become unified into a single automated activity. As requirements change, the project plans and associated tests are adapted to best suit the changes. Now suppose we further embed this adaptivity within the software product itself. Smaller changes to the operating environment can now be handled automatically. Feedback from the operating environment to the development process will also speed adaption of both the software product and process to much larger changes that cannot be handled by such in-situ adaptation. This is the new approach to software engineering DAASE seeks to create. It places computational search at the heart of the processes and products it creates and embeds adaptivity into both. DAASE will also create an array of new processes, methods, techniques and tools for a new kind of software engineering, radically transforming the theory and practice of software engineering. DAASE will develop a hyper-heuristic approach to adaptive automation. A hyper-heuristic is a methodology for selecting or generating heuristics. Most heuristic methods in the literature operate on a search space of potential solutions to a particular problem. However, a hyper-heuristic operates on a search space of heuristics. We do not underestimate the challenges this research agenda poses. However, we believe we have the team, partners and programme plan that will achieve the ambitious aim. DAASE integrates two teams of researchers from the Operational Research and Search Based Software Engineering communities. Both groups of researchers are widely regarded as world leading, having pioneered the fields of Hyper-Heuristics and Search Based Software Engineering (SBSE); the two key fields that DAASE brings together.