- author = "Fabio {de Almeida Farzat} and Marcio {de Oliveira Barros} and Guilherme {Horta Travassos}",
- title = "Challenges on applying genetic improvement in {JavaScript} using a high-performance computer",
- journal = "Journal of Software Engineering Research and Development",
- year = "2018",
- volume = "6",
- number = "12",
- month = dec,
- note = "20th Iberoamerican Conference on Software Engineering",
- keywords = "genetic algorithms, genetic programming, Grammatical evolution, Genetic Improvement, Source code Optimization, Search Based Software Engineering, SBSE",
- ISSN = "2195-1721",
-
DOI = "
doi:10.1186/s40411-018-0056-2",
- size = "19 pages",
- abstract = "Genetic Improvement is an area of Search Based Software Engineering that aims to apply evolutionary computing operators to the software source code to improve it according to one or more quality metrics. This article describes challenges related to experimental studies using Genetic Improvement in JavaScript (an interpreted and non-typed language). It describes our experience on performing a study with fifteen projects submitted to genetic improvement with the use of a supercomputer. The construction of specific software infrastructure to support such an experimentation environment reveals peculiarities (parallelization problems, management of threads, etc.) that must be carefully considered to avoid future research threats to validity such as dead-ends, which make it impossible to observe relevant phenomena (code transformation) to the understanding of software improvements and evolution.",
-
notes = "Lists problems with ECJ ANTLR C-sharp Jurassic project
tree GP NodeJs Typescript
LOBOC supercomputer of COPPE/UFR
Table 1 Thirteen libraries observed. EXECTIMER PUG LODASH MINIMIST MOMENT UNDERSCORE UUID XML2JS PLIVO-NODE GULP-CCCR EXPRESS-IFTTT TLEAF BROWSERIFY
p15 in all cases 'the Optimizer was able to find variations of the original code that run in less time'",
