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Details of Grant 

EPSRC Reference: EP/D050863/1
Title: SEBASE: Software Engineering By Automated SEarch
Principal Investigator: Harman, Professor M
Other Investigators:
Hierons, Professor R Steinhofel, Dr K
Researcher Co-Investigators:
Project Partners:
DaimlerChrysler IBM UK Ltd Motorola
Department: Computer Science
Organisation: Kings College London
Scheme: Standard Research (Pre-FEC)
Starts: 30 June 2006 Ends: 31 July 2010 Value (£): 1,142,565
EPSRC Research Topic Classifications:
Artificial Intelligence Software Engineering
EPSRC Industrial Sector Classifications:
Information Technologies Transport Systems and Vehicles
Related Grants:
Panel History:
Panel DatePanel NameOutcome
19 Sep 2005 SEBASE Visiting Panel Deferred
Summary on Grant Application Form
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.
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