Funding Source: EPSRC
Amount Awarded: £2,262,469
Start Date: 01/10/2015
End Date 30/09/2021
The research has been funded by the EPSRC (Engineering and Physical Sciences Research Council) in response to growing concerns over airport capacity, rising demand, and the impact of congestion on both the travelling public and the air transport industry.
The work will build on the UK's world-leading expertise in Operational Research to find the most efficient ways to schedule flights, developing and testing new models and solution algorithms that take into account all the factors involved in the allocation of flight 'slots': individual airport operations, networks of airports, airline operations, air traffic management systems, airport authorities, civil aviation authorities, airlines and the travelling public.Project Homepage
Amount Awarded: £806,020
Start Date: 01/07/2016
End Date 30/09/2019
TRANSIT (Towards a Robust Airport Decision Support System for Intelligent Taxiing) is a four site project between Queen Mary University of London, The University of Sheffield, University of Stirling and Cranfield Universities.
The research has been funded by the UK EPSRC (grant numbers EP/N029496/2, EP/N029356/1 and EP/N029577/1). The lead of each grant is, respectively, Dr Jun Chen, Professor Mahdi Mahfouf, Dr John Woodward, with Dr Jun Chen from Queen Mary University of London as the overall project director.
The project also has an extensive list of industrial partners, which currently includes Air France – KLM, BAE Systems, Manchester Airport Plc, Rolls-Royce Plc, Simio LLC and Zurich Airport.
The TRANSIT project aims to develop a unified routing and scheduling system which will be more realistic, robust, cost-effective and configurable, producing better conformance of flight crew in response to 4 Dimensional Trajectories.Project Homepage
Funding Source: EPSRC
Amount Awarded: £6,834,903
Start Date: 01/06/2012
End Date 31/05/2019
DAASE (Dynamic Adaptive Automated Software Engineering) is a five site project between University College London, Queen Mary University of London, University of Birmingham, University of Stirling and University of York. The lead at each site is, respectively, Professors Harman, Burke, Yao and Clark and Dr Ochoa, with Professor Harman as the overall project director. The project also has a growing list of industrial partners, which currently includes Air France – KLM, Berner and Mattner, BT Laboratories, DSTL, Ericsson, GCHQ, Honda Research Institute Europe, IBM, Microsoft Research and VISA UK.
DAASE builds on two successful longer larger projects, funded by the EPSRC and which were widely regarded as highly successful and ground breaking. The project also draws inspiration and support from and feeds into the rapidly growing worldwide Search-Based Software Engineering (SBSE) community.
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". Yet software is one of the most inherently flexible engineering materials with which we have worked, DAASE seeks to use computational search as an overall approach to achieve the software's full potential for flexibility and adaptivity. In so-doing we will be creating new ways to develop and deploy software. 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.Project Homepage
Funding Source: EPSRC
Amount Awarded: £853,391
Start Date: 01/11/2018
End Date 31/10/2021
Software is now at the heart of almost everything we do in the world. This software remains largely handmade, and as such, is prone to defects. Testing detects only a sub-set of software defects with the rest laying dormant, sometimes for years. When these defects emerge in software systems the safety and business consequences can be severe. Software failures and their damaging consequences are regularly reported in the press. Finding and fixing defects has been an intransigent problem over many years. The traditional approach to this problem relies on finding defects during testing then developers manually fixing those defects afterwards.
In this project we establish a new technique to automatically fix predicted defects in software code before testing. We use machine learning-based defect prediction information to generate automatic fixes using Genetic Improvement. Our approach aims to offer developers effective fixes to code which is predicted as defective. A higher proportion of the fixes our approach offers to developers should be acceptable, generated quicker and available earlier in the development cycle than previous attempts at automated repair. Importantly, our approach targets a wider pool of defects as it specifically includes targeting those dormant defects which are not identified by testing.
Using our approach the developer will always remain in control of the code produced. Fixes are suggested, and the developer is the 'gate-keeper', deciding if a suggested fix is accepted, rejected, or can itself be modified to improve the code. One of the tangible outputs of the project will be a defect fixing tool (FIXIE), which will provide support to developers in their daily coding activities. The tool will be developed in collaboration with several industrial partners and will be empirically evaluated throughout the project.