Challenges on applying genetic improvement in JavaScript using a high-performance computer

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 J...

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Veröffentlicht in:Journal of software engineering research and development Jg. 6; H. 1; S. 1 - 19
Hauptverfasser: de Almeida Farzat, Fábio, de Oliveira Barros, Márcio, Horta Travassos, Guilherme
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 06.10.2018
Sociedade Brasileira de Computação
Schlagworte:
20th Iberoamerican Conference on Software Engineering
Compilers
Computer Science
Evolutionary algorithms
Experimentation
Information Systems Applications (incl.Internet)
Interpreters
Java
Parallel processing
Programming Languages
Software
Software Engineering
Software Engineering/Programming and Operating Systems
Source code
Source code Optimization
Search Based Software Engineering
Genetic Improvement
ISSN:2195-1721, 2195-1721
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:2195-1721
2195-1721
DOI:10.1186/s40411-018-0056-2