Developing novel methods in the field of software engineering to transfer the challenging and time-consuming task of software specialisation from human to machine.
The project will utilise and develop novel methods in the field of software engineering, called genetic improvement, to transfer the challenging and time-consuming task of software specialisation from human to machine. It will develop novel approaches for specialising and improving efficiency of generalist software for particular application domains in an automated way.
Genetic improvement is a novel field of research that only arose as a standalone area in the last few years. Several factors contributed to the development and success of this field, one of which is the sheer amount of code available online and focus on automated improvement of non-functional properties of software, such as energy or memory consumption.
Dr. Petke is a world-leading expert on genetic improvement, publishing award-winning work on automated software specialisation and transplantation. She won two `Humies' awarded for human-competitive results produced by genetic and evolutionary computation and a best paper award at the International Symposium on Software Testing and Analysis.
This work was also widely covered in media, including the Wired magazine and BBC Click. The potential of genetic improvement for automating certain aspects of the software development process has thus been already recognised in the academic community and beyond.
Dr. Petke will collaborate with a UK-based company, called Satalia, which provides the latest optimisation techniques to the industry. She will apply deliverables of this project to automatically adapt and speed-up their generalist optimisation algorithms to particular classes of real-world problems.
An example is the specialisation of a general routing program to devising an optimal network for broadband connections. Therefore, the deliverables of this project will automate the process of software specialisation for real-world optimisation problems.
This project is funded by the EPSRC.