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EPIC aims to introduce a new way of developing software, as a collaboration between human and machine, exploiting the complementary strengths of each.
EPIC will automatically construct Evolutionary Program Improvement Collaborators (called Epi-Collaborators) that suggest code changes that improve software according to multiple functional and non-functional objectives. The Epi-Collaborator suggestions will include transplantation of code from a donor system to a host, grafting of entirely new features `grown' (evolved) by the Epi-Collaborator, and identification and optimisation of tuneable `deep' parameters (that were previously unexposed and therefore unexploited).
A key feature of the EPIC approach is that all of these suggestions will be underpinned by automatically-constructed quantitative evidence that justifies, explains and documents improvements.
EPIC aims to introduce a new way of developing software, as a collaboration between human and machine, exploiting the complementary strengths of each; the human has domain and contextual insights, while the machine has the ability to intelligently search large search spaces. The EPIC approach directly tackles the emergent challenges of multiplicity: optimising for multiple competing and conflicting objectives and platforms with multiple software versions.
Principle investigator
Research staff
Research Fellows
- Jie Zhang
- Giovanni Guizzo
- Maria Kechagia
Research students
- Dario Asprone
- Max Hort
- Louis Vines
- Rebecca Moussa
- Vali Tawosi
This project is funded by the ERC.