Publication:
A Self-Adapting Ant Colony Optimization Algorithm Using Fuzzy Logic (ACOF) for Combinatorial Test Suite Generation

cris.author.scopus-author-id 57192404100
cris.author.scopus-author-id 36873148100
cris.author.scopus-author-id 57192403982
cris.author.scopus-author-id 57188752016
cris.virtual.department Universiti Malaysia Perlis
cris.virtual.department Universiti Malaysia Perlis
cris.virtual.department Universiti Malaysia Perlis
cris.virtual.department Universiti Malaysia Perlis
cris.virtualsource.department 1c26d10e-78fe-4e76-a033-375e12af629b
cris.virtualsource.department c5b67ba9-e8e6-4575-bd9e-29faa40456ad
cris.virtualsource.department 4fc12880-7e01-4a8f-ad35-06c265c0ef34
cris.virtualsource.department 31b66dd6-2a7a-47f7-8ef9-896fb6e44012
dc.contributor.author Mohd Zamri Zahir Ahmad
dc.contributor.author Rozmie Razif Othman
dc.contributor.author Mohd Shaiful Aziz Rashid Ali
dc.contributor.author Nuraminah Ramli
dc.date.accessioned 2024-10-01T01:01:45Z
dc.date.available 2024-10-01T01:01:45Z
dc.date.issued 2020-03-20
dc.description.abstract Software testing is one of most crucial phase in software development life cycle (SDLC). The main function of testing is to cater bugs between interactions of the inputs. It is not possible to eliminate all bugs in one system but by using a suitable testing optimization, it can provide a good enough solution for it. Reducing effort on this phase is not only could lead to numerous bugs between the input interactions, but it also leads to a greater loss such as loss of profits, reputations and even loss of life. Generally, there are three categories of combinatorial testing techniques which is computational, metaheuristic, and hyper heuristic. Ant colony optimization (ACO) is a heuristic technique where its mimic the nature of ants in finding the best route from the nest to the food node and vice versa. Many optimization problems have been solved by using ACO. This paper is to proposed a self-adapting ant colony optimization algorithm using fuzzy logic (ACOF) for combinatorial test suite generation, where it will dynamically determine number of ants and edge selection (i.e. either to explore or to exploit) based on percentage of remaining tuple list and covered test cases.
dc.identifier.doi 10.1088/1757-899X/767/1/012017
dc.identifier.scopus 2-s2.0-85083110666
dc.identifier.uri https://hdl.handle.net/20.500.14170/6880
dc.language.iso en
dc.relation.funding Ministry of Higher Education, Malaysia
dc.relation.grantno undefined
dc.relation.ispartof IOP Conference Series: Materials Science and Engineering
dc.relation.ispartofseries IOP Conference Series: Materials Science and Engineering
dc.relation.issn 17578981
dc.rights open access
dc.subject Ant Colony Optimization | Combinatorial testing | T-way testing
dc.title A Self-Adapting Ant Colony Optimization Algorithm Using Fuzzy Logic (ACOF) for Combinatorial Test Suite Generation
dc.type Conference Proceeding
dspace.entity.type Publication
oaire.citation.issue 1
oaire.citation.volume 767
oairecerif.affiliation.orgunit Universiti Malaysia Perlis
oairecerif.affiliation.orgunit Universiti Malaysia Perlis
oairecerif.affiliation.orgunit Universiti Malaysia Perlis
oairecerif.affiliation.orgunit Universiti Malaysia Perlis
oairecerif.author.affiliation Universiti Malaysia Perlis
oairecerif.author.affiliation Universiti Malaysia Perlis
oairecerif.author.affiliation Universiti Malaysia Perlis
oairecerif.author.affiliation Universiti Malaysia Perlis
oairecerif.citation.number 012017
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person.identifier.scopus-author-id 57192404100
person.identifier.scopus-author-id 36873148100
person.identifier.scopus-author-id 57192403982
person.identifier.scopus-author-id 57188752016
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