- author = "Brendan Cody-Kenny and Michael O'Neill and Stephen Barrett",
- title = "Performance Localisation",
- booktitle = "GI-2018, ICSE workshops proceedings",
- year = "2018",
- editor = "Justyna Petke and Kathryn Stolee and William B. Langdon and Westley Weimer",
- pages = "27--34",
- 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/Cody-Kenny_2018_GI.pdf",
-
DOI = "
10.1145/3194810.3194815",
- size = "8 pages",
-
abstract = "Profiling techniques highlight where performance
issues manifest and provide a starting point for
tracing cause back through a program. While people
diagnose and understand the cause of performance to
guide formulation of a performance improvement, we seek
automated techniques for highlighting performance
improvement opportunities to guide search
algorithms.
We investigate mutation-based approaches for highlighting where a performance improvement is likely to exist. For all modification locations in a program, we make all possible modifications and analyse how often modifications reduce execution count. We compare the resulting code location rankings against rankings derived using a profiler and find that mutation analysis provides the higher accuracy in highlighting performance improvement locations in a set of benchmark problems, though at a much higher execution cost. We see both approaches as complimentary and consider how they may be used to further guide Genetic Programming in finding performance improvements",
-
notes = "Slides:
http://geneticimprovementofsoftware.com/wp-content/uploads/2018/06/PerformanceLocalisation.pdf
https://github.com/codykenb/locoGP https://codykenb.github.io/locoGP/locoGP-ImprovementsFound.html
GI-2018 http://geneticimprovementofsoftware.com/events/papers#icse2018 part of \cite{Petke:2018:ICSEworkshop}",
