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Evolving Program Improvement Collaborators

Total Cost €


EC-Contrib. €






Project "EPIC" data sheet

The following table provides information about the project.


Organization address
city: LONDON
postcode: WC1E 6BT
website: n.a.

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country United Kingdom [UK]
 Total cost 2˙159˙035 €
 EC max contribution 2˙159˙035 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2016-ADG
 Funding Scheme ERC-ADG
 Starting year 2017
 Duration (year-month-day) from 2017-10-01   to  2022-09-30


Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    UNIVERSITY COLLEGE LONDON UK (LONDON) coordinator 2˙159˙035.00


 Project objective

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.

Keywords: Search Based Software Engineering (SBSE), Evolutionary Computing, Software Testing, Genetic Algorithms, Genetic Programming.


year authors and title journal last update
List of publications.
2018 Marinos Kintis, Mike Papadakis, Yue Jia, Nicos Malevris, Yves Le Traon, Mark Harman
Detecting Trivial Mutant Equivalences via Compiler Optimisations
published pages: 308-333, ISSN: 0098-5589, DOI: 10.1109/tse.2017.2684805
IEEE Transactions on Software Engineering 44/4 2019-08-05
2018 Justyna Petke, Mark Harman, William B. Langdon, Westley Weimer
Specialising Software for Different Downstream Applications Using Genetic Improvement and Code Transplantation
published pages: 574-594, ISSN: 0098-5589, DOI: 10.1109/tse.2017.2702606
IEEE Transactions on Software Engineering 44/6 2019-08-05
2018 Yuanyuan Zhang, Mark Harman, Gabriela Ochoa, Guenther Ruhe, Sjaak Brinkkemper
An Empirical Study of Meta- and Hyper-Heuristic Search for Multi-Objective Release Planning
published pages: 1-32, ISSN: 1049-331X, DOI: 10.1145/3196831
ACM Transactions on Software Engineering and Methodology 27/1 2019-08-05
2018 Justyna Petke, Saemundur O. Haraldsson, Mark Harman, William B. Langdon, David R. White, John R. Woodward
Genetic Improvement of Software: A Comprehensive Survey
published pages: 415-432, ISSN: 1089-778X, DOI: 10.1109/tevc.2017.2693219
IEEE Transactions on Evolutionary Computation 22/3 2019-08-05
2018 Matheus Paixao, Mark Harman, Yuanyuan Zhang, Yijun Yu
An Empirical Study of Cohesion and Coupling: Balancing Optimization and Disruption
published pages: 394-414, ISSN: 1089-778X, DOI: 10.1109/tevc.2017.2691281
IEEE Transactions on Evolutionary Computation 22/3 2019-08-05

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The information about "EPIC" are provided by the European Opendata Portal: CORDIS opendata.

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