SEBASE: Software Engineering By Automated SEarch

Lead Research Organisation: King's College London
Department Name: Computer Science

Abstract

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.

Publications

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Related Projects

Project Reference Relationship Related To Start End Award Value
EP/D050863/1 30/06/2006 31/07/2010 £1,142,565
EP/D050863/2 Transfer EP/D050863/1 01/08/2010 31/12/2011 £402,063
 
Description This grant is identical to the grant with the code EP/D050863/2. this is why the two grants of been grouped together, but there is to be no way to actually merge them.
Exploitation Route This grant is identical to the grant with the code EP/D050863/2. this is why the two grants of been grouped together, but there is to be no way to actually merge them.
Sectors Other

 
Description This grant is identical to the grant with the code EP/D050863/2. this is why the two grants of been grouped together, but there is to be no way to actually merge them.
 
Title GP Bibliography 
Description The GP Bibliography is a repository of all publications on the topic of genetic programming, which is maintained by Bill Langdon (William B. Langdon). The repository has been available since before 2006, but since 2011, its maintenance has been supported by the EPSRC project GISMO, which funds, in full, Dr Langdon. It was started by Dr langdon when he was at the University of Birmingham, though he has been at University College London since 2010. The University of Birmingham continues to host the repository, while support for its maintenance and update by Dr langdon comes from UCL, through GISMO Project. Before the GISMO Project, Dr langdon was funded by the CREST platform grant and SEBASE projects. 
Type Of Material Database/Collection of data 
Provided To Others? Yes  
Impact Repository contains over 7000 entries, and is widely used by other researchers. It is the first point of call for any researcher working in genetic programming, in order to search for and find relevant information on previous research in this area. 
URL http://www.cs.bham.ac.uk/~wbl/biblio/
 
Title SBSE repository 
Description This collects the work which address the software engineering problems using metaheuristic search optimisation techniques (i. e. Genetic Algorithms) into the Repository of Publications on Search Based Software Engineering 
Type Of Material Database/Collection of data 
Year Produced 2010 
Provided To Others? Yes  
Impact This repository is the first point of contact for all researchers working in search based software engineering. It has been used by a number of other researchers in systematic literature reviews, as a source of comprehensive information regarding all papers on this topic. It contains over 1200 entries, and lists over 1500 different researchers. A number of different analyses have been built on top of the repository, and it has been used by many researchers in the construction of their related work. 
URL http://crestweb.cs.ucl.ac.uk/resources/sbse_repository/
 
Title AUSTIN 
Description AUSTIN is a structural test data generation tool (for unit tests) for the C language. It is designed as a research prototype and the aim of this project is to aid researchers in automated test data generation using search-based algorithms. It is based on the CIL framework and currently supports a random search, as well as a simple hill climber that is augmented with a set of constraint solving rules for pointer type inputs. 
Type Of Technology Software 
Year Produced 2011 
Open Source License? Yes  
Impact We don't track users of the tool, but we have become aware of several organisations, both academic and industrial where the tool has been used through email correspondence. 
URL https://code.google.com/p/austin-sbst/
 
Title Milu 
Description Milu is an efficient and flexible C mutation testing tool designed for both first order and highe order mutation testing. The name 'Milu' is from a deer composed of four other animals. It has a horse's head, a deer's antlers, a donkey's body and a cow's hooves. 
Type Of Technology Software 
Year Produced 2009 
Open Source License? Yes  
Impact This tool has been used by many other researchers working on mutation testing. It is featured in several places by other authors, including journals. We do not keep tabs on those who use the tool, so have no direct available information about industrial use. We only become aware of its use when it is acknowledged in a research paper. 
URL http://www0.cs.ucl.ac.uk/staff/Y.Jia/Milu/