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Semmle is a successful spin-out company set up by members of the UoA, based on their research on program analysis. Semmle markets an industrial-strength product allowing organisations with large software systems to understand and manage their code bases. This business intelligence platform started to be sold to prominent customers in 2008, including [text removed for publication] NASA. NASA used it to help ensure the safe landing of the Curiosity Mars Rover.
Transport crew scheduling research at Leeds University since 1994 produced optimising algorithms and industry-ready software that led to the spinning out of Tracsis in 2004. The software, including upgrades, is used by over 40 bus and train companies who previously relied on manual processes. A minimum estimate of a £230 million saving in crew costs has been achieved in the UK alone over 2008-31.7.2013. Since 2008, the software has been routinely used by bidders in all UK rail franchise tenders, contributing to cost effective, efficient and reliable rail transport. Success led to the Tracsis floatation in November 2007 (market capitalisation: £46.7 million on 22/5/2013).
The spin-out company CSM Ltd. was set up in 1991 to commercially develop Durham research on program transformation. Up until 1999, this company (which in the mid-90's became Durham Software Engineering Ltd. and subsequently Software Migrations Ltd.) and researchers at Durham University developed the FermaT Workbench: an industrial-strength assembler re-engineering workbench for program comprehension, migration and re-engineering. In 1999, Software Migrations Ltd. relocated to St. Albans and now has an extensive list of national and international clients. All its products (software and services) are built on the FermaT Workbench and has generated considerable revenue with this revenue strongly expected to rise steeply in the near future.
Essex research into the practical deployment of computational grammar theories, tools and techniques led to the expertise of Dr Doug Arnold being sought between 2009 and 2011 by BAE Systems, a leading UK manufacturer of advanced defence and security systems. Arnold advised the company on the design of two prototype natural-language interfaces for responding to emergency situations and sharing sensitive data across organisations. The projects' goals were met and his contribution enabled BAE Systems to develop feasibility-of-concept demonstration systems. His practical expertise in Natural Language Processing provided the company with an appreciation of the limits of particular tools and helped it to avoid undertaking over-ambitious projects.
Memory violations are a major cause of security breaches and operational flaws in today's software systems. Proving memory safety was traditionally a core challenge in program verification due to the high complexity of reasoning about pointer manipulations. Researchers at Queen Mary and Imperial jointly produced breakthrough algorithms for automatically reasoning about pointers, enabling highly-scalable automatic verification for industrial code. These techniques resulted in the industrial program analysis tool INFER developed by Monoidics Ltd, and used by customers across the world. The verification algorithms developed at Queen Mary and Imperial were also incorporated in Microsoft tools used to secure Windows device drivers.
Embedded software in the transportation sector (railway, automotive and avionics) needs to meet high reliability requirements because errors may have severe consequences. Research since 2008 in the UoA has developed effective reasoning technology to provide assurance that key error types are eliminated from embedded software, and has created novel algorithms to prove its integrity. Major players such as [text removed for publication] GM and Airbus have used technology developed in the UoA to verify the absence of errors. A particular advantage of this technology is its ability to reason about floating-point arithmetic, meaning that a much wider class of properties can be verified. The technology is widely distributed via third party operating systems and tool-sets.
Graph-theoretic and mathematically rigorous algorithmic methods developed at the University of Hertfordshire have improved the applicability of compiler technology and parallel processing. A compiler developed in the course of a ten-year research programme at the university has been successfully applied to a number of commercial problems by re-purposing the research tool. NAG Ltd has adapted the tool into a commercial product [text removed for publication]. Numerous applications of the mathematical methods (such as type-flow graphs used conjointly for correctness and optimisation) have been deployed by industry (including SAP, SCCH, German Waterways Board) working closely with the university.
Greenfoot is a software system to support the learning of programming at school level (age 13 upwards). During the REF period, over a million students worldwide have learned programming through Greenfoot: at school, in after school clubs and workshops, and privately at home. Greenfoot has helped to raise the profile of programming in schools and outside in a number of countries. The research described here has had impact on a variety of stakeholders, including pupils, teachers and those involved in national curriculum development. Greenfoot is currently downloaded more than 350,000 times/year and is in active use in thousands of schools. Greenfoot is one of very few systems, internationally, to have this level of impact on programming education.
Bishop and Danicic contributed to the development of novel spend analysis software. Launched in 2011 as a commercial service by KTP industrial partners @UK PLC, SpendInsight has been used by over 380 organisations, including Basingstoke and North Hampshire NHS Foundation Trust, which, alone, cut procurement spend by £300,000 via savings identified using SpendInsight. An analysis produced by SpendInsight for the National Audit Office identified gross inefficiencies in NHS procurement, yielding potential annual overall savings of at least £500 million. The findings of this report were discussed in parliament and changes to NHS purchasing policy were recommended as a result.
The world is facing challenges in feeding its growing population. Climate change and increasing urbanisation have led to estimates of a 50% increase required in food production by 2050 according to report of the FAO on food security in 2012. Agriscience research has been developing high yielding crop varieties, which in turn, requires integration of incomplete complex data sets. Research in machine learning and predictive modelling at Imperial has addressed these challenges by filling the gaps in the descriptions of biological networks. This is having a significant economic impact in agriscience areas such as tomato ripening (market value > $100m), herbicide toxicity (market value ca. $20bn) and environmental modelling of herbicide-based crop management (> $100m).