abstract = "We present a Genetic Improvement (GI) experiment on
ProbAbel, a piece of bioinformatics software for Genome
Wide Association (GWA) studies. The GI framework used
here has previously been successfully used on Python
programs and can, with minimal adaptation, be used on
source code written in other languages. We achieve
improvements in execution time without the loss of
accuracy in output while also exploring the vast
fitness landscape that the GI framework has to search.
The runtime improvements achieved on smaller data set
scale up for larger data sets. Our findings are that
for ProbAbel, the GI's execution time landscape is
noisy but flat. We also confirm that human written code
is robust with respect to small edits to the source
notes = "missing values, SNPs. Learn from smallest dataset but
mutated C code applicable to larger dataset. Macro
mutation: moving lines of code (eg delete line 321) and
micromutation, in statement token changes, eg add one
to integer constant, Replace col_new++ with ++col_new.
GI run 8 hours. No semantic change. 'Software is Not
Fragile' Significant but tiny speedup.