Multi-Objective Improvement of Android Applications

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Misc{callan2023multiobjective,

• author = "James Callan and Justyna Petke",
• title = "Multi-Objective Improvement of Android Applications",
• howpublished = "arXiv",
• year = "2023",
• month = "22 " # aug,
• note = "arxiv, 2308.11387",
• keywords = "genetic algorithms, genetic programming, genetic improvement, multi-objective optimization, Android apps, search-based software engineering, SBSE, mobile computing, GIDroid, Java",
• eprint = "2308.11387",
• archiveprefix = "arXiv",
• primaryclass = "cs.SE",
• URL = "https://arxiv.org/abs/2308.11387",
• code_url = "https://github.com/SOLAR-group/GIDroid",
• size = "32 pages",
• abstract = ".. we use Genetic improvement, a search-based technique that navigates the space of software variants to find improved software. We use a simulation-based testing framework to greatly improve the speed of search. GIDroid contains three state-of-the-art multi-objective algorithms, and two new mutation operators, which cache the results of method calls. Genetic improvement relies on testing to validate patches. Previous work showed that tests in open-source Android applications are scarce. We thus wrote tests for 21 versions of 7 Android apps, creating a new benchmark for performance improvements. We used GIDroid to improve versions of mobile apps where developers had previously found improvements to runtime, memory, and bandwidth use. Our technique automatically re-discovers 64percent of existing improvements. We then applied our approach to current versions of software in which there were no known improvements. We were able to improve execution time by up to 35percent, and memory use by up to 33percent in these apps.",
• notes = "?to appear in? Automated Software Engineering ?",

}

Genetic Programming entries for James Callan Justyna Petke

Citations