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dc.contributor.authorPatipat Konsaarden_US
dc.contributor.authorLachana Ramingwongen_US
dc.date.accessioned2018-09-05T02:57:45Z-
dc.date.available2018-09-05T02:57:45Z-
dc.date.issued2016-01-04en_US
dc.identifier.other2-s2.0-84964680319en_US
dc.identifier.other10.1109/ICISSEC.2015.7371038en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84964680319&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/55545-
dc.description.abstract© 2015 IEEE. The goal of test suite prioritization is maximizing fault detection and code coverage rate. Several nature inspired optimization algorithms such as Swarm Intelligence (SI) have been studied for the optimization of such problems. The studies revealed the benefits of Artificial Bee Colony (ABC) over other algorithms. ABC and its variations were implemented in software testing areas, test suite prioritization in particular. However, most SI based approaches focus on fault detection ability which is difficult to predict. In this paper, the standard ABC algorithm is used to prioritize test suites based on code coverage. The results reveal that ABC shows promising results and, hence, is a great candidate for prioritizing test suites. It also suggests that a modification to the standard ABC algorithm or combination of ABC and another SI algorithm should yield an even better result.en_US
dc.subjectComputer Scienceen_US
dc.subjectEngineeringen_US
dc.titleUsing artificial bee colony for code coverage based test suite prioritizationen_US
dc.typeConference Proceedingen_US
article.title.sourcetitle2015 IEEE 2nd International Conference on InformationScience and Security, ICISS 2015en_US
article.stream.affiliationsChiang Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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