Current software engineering practice is a human-led search for solutions which meet needs and constraints under limited resources. Often there will be conflict, both between and within functional and non-functional criteria. Naturally, like other engineers, we search for a near optimal solution. As systems get bigger, more distributed, more dynamic and more critical, this labour-intensive search will hit fundamental limits. We will not be able to continue to develop, operate and maintain systems in the traditional way, without automating or partly automating the search for near optimal solutions. Automated search based solutions have a track record of success in other engineering disciplines, characterised by a large number of potential solutions, where there are many complex, competing and conflicting constraints and where construction of a perfect solution is either impossible or impractical. The SEMINAL network demonstrated that these techniques provide robust, cost-effective and high quality solutions for several problems in software engineering. Successes to date can be seen as strong pointers to search having great potential to serve as an overarching solution paradigm. The SEBASE project aims to provide a new approach to the way in which software engineering is understood and practised. It will move software engineering problems from human-based search to machine-based search. As a result, human effort will move up the abstraction chain, to focus on guiding the automated search, rather than performing it. This project will address key issues in software engineering, including scalability, robustness, reliability and stability. It will also study theoretical foundations of search algorithms and apply the insights gained to develop more effective and efficient search algorithms for large and complex software engineering problems. Such insights will have a major impact on the search algorithm community as well as the software engineering community.

Current software engineering practice is a human-led search for solutions which meet needs and constraints under limited resources. Often there will be conflict, both between and within functional and non-functional criteria. Naturally, like other engineers, we search for a near optimal solution. As systems get bigger, more distributed, more dynamic and more critical, this labour-intensive search will hit fundamental limits. We will not be able to continue to develop, operate and maintain systems in the traditional way, without automating or partly automating the search for near optimal solutions. Automated search based solutions have a track record of success in other engineering disciplines, characterised by a large number of potential solutions, where there are many complex, competing and conflicting constraints and where construction of a perfect solution is either impossible or impractical. The SEMINAL network demonstrated that these techniques provide robust, cost-effective and high quality solutions for several problems in software engineering. Successes to date can be seen as strong pointers to search having great potential to serve as an overarching solution paradigm. The SEBASE project aims to provide a new approach to the way in which software engineering is understood and practised. It will move software engineering problems from human-based search to machine-based search. As a result, human effort will move up the abstraction chain, to focus on guiding the automated search, rather than performing it. This project will address key issues in software engineering, including scalability, robustness, reliability and stability. It will also study theoretical foundations of search algorithms and apply the insights gained to develop more effective and efficient search algorithms for large and complex software engineering problems. Such insights will have a major impact on the search algorithm community as well as the software engineering community.

Current software engineering practice is a human-led search for solutions which meet needs and constraints under limited resources. Often there will be conflict, both between and within functional and non-functional criteria. Naturally, like other engineers, we search for a near optimal solution. As systems get bigger, more distributed, more dynamic and more critical, this labour-intensive search will hit fundamental limits. We will not be able to continue to develop, operate and maintain systems in the traditional way, without automating or partly automating the search for near optimal solutions. Automated search based solutions have a track record of success in other engineering disciplines, characterised by a large number of potential solutions, where there are many complex, competing and conflicting constraints and where construction of a perfect solution is either impossible or impractical. The SEMINAL network demonstrated that these techniques provide robust, cost-effective and high quality solutions for several problems in software engineering. Successes to date can be seen as strong pointers to search having great potential to serve as an overarching solution paradigm. The SEBASE project aims to provide a new approach to the way in which software engineering is understood and practised. It will move software engineering problems from human-based search to machine-based search. As a result, human effort will move up the abstraction chain, to focus on guiding the automated search, rather than performing it. This project will address key issues in software engineering, including scalability, robustness, reliability and stability. It will also study theoretical foundations of search algorithms and apply the insights gained to develop more effective and efficient search algorithms for large and complex software engineering problems. Such insights will have a major impact on the search algorithm community as well as the software engineering community.

Introducing porosity onto an aerofoil has been shown to have a significant influence on the boundary layer and provide significant reductions in its noise radiation. This proposal describes a multi-disciplinary research project aimed at understanding and exploiting the interactions between porous aerofoils and the boundary layers developing over them for the purpose of optimising noise reductions without compromising aerodynamic performance. The use of adaptive manufacturing technology will be investigated for providing the optimum porosity at different operating conditions.

This project will, for the first time, connect a detailed scientific understanding of the mechanisms of coatings failure with state-of-the-art machine learning to deliver a design framework for the optimization of protective coatings and nanocomposite materials. It will be game changing for an industry (paint) which is often taken for granted, despite its ubiquity - the screen you are looking at, the color of your car, the protection for the aircraft you fly in, the longevity of bridges, wind turbine masts and other infrastructure. Indeed, almost all materials are made suitable for purpose or given function by the application of coatings. In the UK there are over 10,000 employees involved in manufacturing coatings and the coatings industry directly contributes over £11bn to the economy, supporting UK manufacturing and construction sectors worth around £150bn. The annual costs of corrosion damage in the UK lies in the range of 2-3% of Gross National Product (~£60 bn, 2016) and leads to premature loss of amenity in infrastructure and equipment; hence to environmental damage through accelerated extraction and resource use. Protective organic coatings (i.e. paints) are highly cost effective in limiting early materials damage due to corrosion however these are complex products where the underlying mechanistic links between the formulation and performance are lacking. The increasing need to use environmentally sustainable materials, reduce time-to-market and increase performance requires detailed mechanistic understanding across functions and length scales from the molecular to the macroscopic. With brands such as Dulux, Hammerite and International, AkzoNobel are one of the world's largest manufacturers of protective and decorative coatings and have extensive manufacturing and research operations in the UK. AkzoNobel invests heavily in research, both in its global research hub for performance coatings in the NE of England as well as in UK universities. In particular the company (and its predecessor bodies) has collaborated in polymer science with the University of Sheffield, and in corrosion protection with The University of Manchester, for over 30 years. This prosperity partnership between EPSRC and AkzoNobel/ International Paint with the Universities of Manchester and Sheffield, will enable for the 1st time, a fundamental mechanistic understanding of how the performance of protective organic coatings arises - essentially it will tell us "how paint works". The scope of the program is well beyond the capacity of an individual company, institution or funder and, hence, the collaborative partnership is essential in order to tackle this problem head-on. Success will allow industry to side-step the current trial-and-error approaches and to incorporate digital design (i.e. Industry 4.0) into the development of paints and similar nanocomposite materials resulting in the confidence to utilize sustainable materials, comply with legislative and customer drivers and maintain and extend performance in more extreme environments. Overall the project will deliver understanding and tools that underpin the rapid-to-market development of environmentally sustainable protective organic coatings and nanocomposites by rational design.