- author = "Jason Landsborough and Stephen Harding and Sunny Fugate",
- title = "Learning from Super-Mutants: searching post-apocalyptic software ecosystems for novel semantics-preserving transforms",
- booktitle = "GI-2017",
- year = "2017",
- editor = "Justyna Petke and David R. White and W. B. Langdon and Westley Weimer",
- pages = "1529--1536",
- address = "Berlin",
- month = "15-19 " # jul,
- publisher = "ACM",
- keywords = "genetic algorithms, genetic programming, genetic improvement, search based software engineering, SBSE, validation, Modification of Binary Files",
- isbn13 = "978-1-4503-4939-0",
-
URL = "
http://geneticimprovementofsoftware.com/wp-content/uploads/2017/05/landsborough2017_supermutants.pdf",
-
DOI = "
doi:10.1145/3067695.3082525",
- size = "8 pages",
- abstract = "In light of recent advances in genetic-algorithm-driven automated program modification, our team has been actively exploring the art, engineering, and discovery of novel semantics-preserving transforms. While modern compilers represent some of the best ideas we have for automated program modification, current approaches represent only a small subset of the types of transforms which can be achieved. In the wilderness of post-apocalyptic software ecosystems of genetically-modified and mutant programs, there exist a broad array of potentially useful software mutations, including semantics-preserving transforms that may play an important role in future software design, development, and most importantly, evolution.",
-
notes = "Mutations (and linear 2pt crossover)to 86x binary
machine code generated by GCC (without optimisation).
16/107 examples from gnu unix coreutils 8.25. Copy,
delete and swap mutation. Sandboxing provided by
distributing tests across sixteen 2GB virtual machines
each with 2 CPU cores. Half a million fitness
evaluations in two-seven days. Blind mutations,
probably to code .text section(?) but no information on
if mutated code is exercised by test suite or not.
SQLite 'our testing approach is sufficient to ensure
that a transform is safe in terms of whole-program
validity.'
Section 4.1 Novel Transforms 'transform short circuits the call to close stdout by simply loading the expected return address into rax and leaving stdout open.' 'error handling is simply replaced with NOPs'
'5 CONCLUSIONS The application of genetic programming to software repair, optimization, and general modification have proven incredibly fruitful.'",
