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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Vydáno v:Journal of software engineering research and development Ročník 6; číslo 1; s. 1 - 19
Hlavní autoři: de Almeida Farzat, Fábio, de Oliveira Barros, Márcio, Horta Travassos, Guilherme
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 06.10.2018
Sociedade Brasileira de Computação
Témata:
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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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.
AbstractList 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.
ArticleNumber 12
Author de Almeida Farzat, Fábio
Horta Travassos, Guilherme
de Oliveira Barros, Márcio
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10.1007/978-3-642-29044-2
10.1007/978-3-642-03237-0_17
10.1007/11531142
10.1145/2351676.2351678
10.1007/978-3-319-09940-8_19
10.1109/TEVC.2010.2052622
10.1007/978-3-540-89694-4_7
10.1145/2739482.2768419
10.1109/ICSE.2012.6227211
10.1145/1145319.1145331
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Journal of Software Engineering Research and Development is a copyright of Springer, (2018). All Rights Reserved. © 2018. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
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Keywords Source code Optimization
Search Based Software Engineering
Genetic Improvement
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– reference: BlackAndrew P.ECOOP 2005 - Object-Oriented Programming2005Berlin, HeidelbergSpringer Berlin Heidelberg
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– reference: White DR (2010) Genetic Programming for Low-Resource Systems. PhD Thesis, Dapartment of Computer Science, University of York, UK
– reference: PetkeJLangdonWBHarmanMApplying genetic improvement to MINISAT2013St Petersburgh, RUProceedings of the Symposium on 6th Search Based Software Engineering10.1007/978-3-642-39742-4_21
– reference: WohlinClaesRunesonPerHöstMartinOhlssonMagnus C.RegnellBjörnWesslénAndersExperimentation in Software Engineering2012Berlin, HeidelbergSpringer Berlin Heidelberg10.1007/978-3-642-29044-2
– reference: BiermanGAbadiMTorgersenMUnderstanding typescript2014Genova, ITProceedings of the 23rd European Conference on Object-Oriented Programming10.1007/978-3-662-44202-9_11
– reference: PetkeJustynaHaraldssonSaemundur O.HarmanMarkLangdonWilliam B.WhiteDavid R.WoodwardJohn R.Genetic Improvement of Software: A Comprehensive SurveyIEEE Transactions on Evolutionary Computation201822341543210.1109/TEVC.2017.2693219
– reference: OrlovMSipperMFlight of the FINCH through the Java wildernessIEEE Transactions Evolutionary Computation201115216618210.1109/TEVC.2010.2052622
– reference: Marques-Silva J, Lynce I, Malik S (2009) Conflict-driven clause learning SAT solvers. Frontiers in Artificial Intelligence and Applications. http://doi.org/10.3233/978-1-58603-929-5-131
– reference: Cody-Kenny B, Lopez EG, Barrett S (2015) locoGP: Improving Performance by Genetic Programming Java Source Code. In Genetic Improvement 2015 Workshop. http://doi.org/doi:10.1145/2739482.2768419
– reference: Overbey J, Xanthos S, Johnson R, Foote B (2005) Refactorings for Fortran and high-performance computing. In Proceedings of the second international workshop on Software engineering for high performance computing system applications - SE-HPCS ’05. http://doi.org/10.1145/1145319.1145331
– reference: de Almeida Farzat F, Travassos G, de Oliveira Barros M (2017) Desafios para o Planejamento e Execução de Experimentos Utilizando Melhoramento Genético em JavaScript. In: XX Ibero-American Conference on Software Engineering. p. 525–538
– reference: HarmanMarkIslamSyedJiaYueMinkuLeandro L.SarroFedericaSrivisutKomsanLess is More: Temporal Fault Predictive Performance over Multiple Hadoop ReleasesSearch-Based Software Engineering2014ChamSpringer International Publishing240246
– reference: JensenSimon HolmMøllerAndersThiemannPeterType Analysis for JavaScriptStatic Analysis2009Berlin, HeidelbergSpringer Berlin Heidelberg23825510.1007/978-3-642-03237-0_17
– reference: ArcuriAndreaWhiteDavid RobertClarkJohnYaoXinMulti-objective Improvement of Software Using Co-evolution and Smart SeedingLecture Notes in Computer Science2008Berlin, HeidelbergSpringer Berlin Heidelberg6170
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SubjectTerms 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
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