- author = "Aymeric Blot",
- title = "Fuzzy Edit Sequences in Genetic Improvement",
- booktitle = "GI-2019, ICSE workshops proceedings",
- year = "2019",
- editor = "Justyna Petke and Shin Hwei Tan and William B. Langdon and Westley Weimer",
- pages = "30--31",
- address = "Montreal",
- month = "28 " # may,
- publisher = "IEEE",
- keywords = "genetic algorithms, genetic programming, genetic improvement, GI, SBSE, search-based software engineering, fuzzy matching",
- isbn13 = "978-1-7281-2268-7",
-
URL = "
http://www.cs.ucl.ac.uk/staff/a.blot/files/blot_gi-icse_2019.pdf",
-
URL = "http://geneticimprovementofsoftware.com/paper_pdfs/blot2019gi.pdf",
-
DOI = "
10.1109/GI.2019.00016",
- size = "2 pages",
- abstract = "Genetic improvement uses automated search to find improved versions of existing software. Edit sequences have been proposed as a very convenient way to represent code modifications, focusing on the changes themselves rather than duplicating the entire program. However, edits are usually defined in terms of practical operations rather than in terms of semantic changes; indeed, crossover and other edit sequence mutations usually never guarantee semantic preservation. We propose several changes to usual edit sequences, specifically augmenting edits with content data and using fuzzy matching, in an attempt to improve semantic preservation.",
-
notes = "GI-2019 http://geneticimprovementofsoftware.com
Slides:
http://www.cs.ucl.ac.uk/staff/a.blot/files/blot_gi-icse_2019_slides.pdf
part of \cite{Petke:2019:ICSEworkshop}",
