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Coevolution of Second-order-mutant

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 8 No. 5 (2018)

DOI
10.11591/ijece.v8i5.pp3238-3249
ISSN
2088-8708, 2088-8708
Published
2018-10-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-nc/4.0

Abstract

One of the obstacles that hinder the usage of mutation testing is its impracticality, two main contributors of this are a large number of mutants and a large number of test cases involves in the process. Researcher usually tries to address this problem by optimizing the mutants and the test case separately. In this research, we try to tackle both of optimizing mutant and optimizing test-case simultaneously using a coevolution optimization method. The coevolution optimization method is chosen for the mutation testing problem because the method works by optimizing multiple collections (population) of a solution. This research found that coevolution is better suited for multi-problem optimization than other single population methods (i.e. Genetic Algorithm), we also propose new indicator to determine the optimal coevolution cycle. The experiment is done to the artificial case, laboratory, and also a real case.

Collaboration network of the authors

17 authors · 30 co-authorship links · 4 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Bandung Institute of Technology
  • HAN University of Applied Sciences
  • Parahyangan Catholic University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Fransiskus AdikaraBandung Institute of Technology4
Gusti Ayu Putri SaptawatiBandung Institute of Technology2
Suhono Harso SupangkatBandung Institute of Technology1
Ahmad DahlanBandung Institute of Technology1
Dwi Hendratmo WidyantoroBandung Institute of Technology1
Fazat Nur AzizahBandung Institute of Technology1
Guido P. BakemaHAN University of Applied Sciences1
Oerip S. SantosoBandung Institute of Technology1
Masayu Leylia KhodraBandung Institute of Technology1
Nasy`an Taufiq Al GhifariBandung Institute of Technology1
Veronica Sri MoertiniParahyangan Catholic University1
Oerip S. SantosaAffiliation unknown1
Regina Yulia YasminBandung Institute of Technology1
Putri SaptawatiBandung Institute of Technology1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>Coevolution of Second-order-mutant</dc:title><dc:creator>Tuloli, Mohamad Syafri</dc:creator><dc:creator>Sitohang, Benhard</dc:creator><dc:creator>Hendradjaya, Bayu</dc:creator><dc:description>&lt;jats:p&gt;&amp;lt;span&amp;gt;One of the obstacles that hinder the usage of mutation testing is its impracticality, two main contributors of this are a large number of mutants and a large number of test cases involves in the process. Researcher usually tries to address this problem by optimizing the mutants and the test case separately. In this research, we try to tackle both of optimizing mutant and optimizing test-case simultaneously using a coevolution optimization method. The coevolution optimization method is chosen for the mutation testing problem because the method works by optimizing multiple collections (population) of a solution. This research found that coevolution is better suited for multi-problem optimization than other single population methods (i.e. Genetic Algorithm), we also propose new indicator to determine the optimal coevolution cycle. The experiment is done to the artificial case, laboratory, and also a real case.&amp;lt;/span&amp;gt;&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2018-10-01</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.11591/ijece.v8i5.pp3238-3249</dc:identifier><dc:identifier>10.11591/ijece.v8i5.pp3238-3249</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 8 No 5 (2018); 3238</dc:source><dc:source>2088-8708</dc:source><dc:source>2088-8708</dc:source><dc:relation>http://ijece.iaescore.com/index.php/IJECE/article/viewFile/10478/11021</dc:relation><dc:rights>http://creativecommons.org/licenses/by-nc/4.0</dc:rights></oai_dc:dc>