- author = "Jason Landsborough and Stephen Harding and Bryan Beabout",
- title = "Evolutionary Fuzzing for Genetic Improvement: Toward Adaptive Software Defense",
- booktitle = "GI-2018, ICSE workshops proceedings",
- year = "2018",
- editor = "Justyna Petke and Kathryn Stolee and William B. Langdon and Westley Weimer",
- pages = "45--46",
- address = "Gothenburg, Sweden",
- month = "2 " # jun,
- publisher = "ACM",
- publisher_address = "New York, NY, USA",
- keywords = "genetic algorithms, genetic programming, genetic improvement",
- isbn13 = "978-1-4503-5753-1",
-
URL = "
http://www.cs.ucl.ac.uk/staff/W.Langdon/icse2018/gi2018/papers/Landsborough_2018_GI.pdf",
-
DOI = "
doi:10.1145/3194810.3194819",
- size = "2 pages",
- abstract = "As fuzz testing strategies have become more and more sophisticated, we see a natural application of fuzz testing to Genetic Improvement techniques. In particular, the ability to generate high quality and high coverage tests with advanced fuzzers can greatly enhance the effectiveness of Genetic Improvement algorithms, especially when the algorithm is applied to bug fixing or other similar kinds of software improvement to improve qualities such as security.",
-
notes = "Slides:
http://geneticimprovementofsoftware.com/wp-content/uploads/2018/06/Landsborough_GI_2018_Slides.compressed.pdf
GI-2018 http://geneticimprovementofsoftware.com/events/papers#icse2018 part of \cite{Petke:2018:ICSEworkshop}",
