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Conference Paper: The ART of divide and conquer: an innovative approach to improving the efficiency of adaptive random testing

TitleThe ART of divide and conquer: an innovative approach to improving the efficiency of adaptive random testing
Authors
KeywordsAdaptive random testing
Divide and conquer
Efficiency
Effectiveness
Software testing
Issue Date2013
PublisherIEEE Computer Society.
Citation
Symposium on Engineering Test Harness, Nanjing, China, July 29-30, 2013. In Proceedings: 13th International Conference on Quality Software (QSIC), Nanjing, China, 29-30 July 2013, p. 268-275 How to Cite?
AbstractTest case selection is a prime process in the engineering of test harnesses. In particular, test case diversity is an important concept. In order to achieve an even spread of test cases across the input domain, Adaptive Random Testing (ART) was proposed such that the history of previously executed test cases are taken into consideration when selecting the next test case. This was achieved through various means such as best candidate selection, exclusion, partitioning, and diversity metrics. Empirical studies showed that ART algorithms make good use of the concept of even spreading and achieve 40 to 50% improvement in test effectiveness over random testing in revealing the first failure, which is close to the theoretical limit. However, the computational complexity of ART algorithms may be quadratic or higher, and hence efficiency is an issue when a large number of previously executed test cases are involved. This paper proposes an innovative divide-and-conquer approach to improve the efficiency of ART algorithms while maintaining their performance in effectiveness. Simulation studies have been conducted to gauge its efficiency against two most commonly used ART algorithms, namely, fixed size candidate set and restricted random testing. Initial experimental results show that the divide-and-conquer technique can provide much better efficiency while maintaining similar, or even better, effectiveness.
DescriptionCo-located with 13th International Conference on Quality Software (QSIC), Nanjing, China, 29-30 July 2013
Persistent Identifierhttp://hdl.handle.net/10722/184863
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorChow, Cen_US
dc.contributor.authorChen, TYen_US
dc.contributor.authorTse, THen_US
dc.date.accessioned2013-07-15T10:14:44Z-
dc.date.available2013-07-15T10:14:44Z-
dc.date.issued2013en_US
dc.identifier.citationSymposium on Engineering Test Harness, Nanjing, China, July 29-30, 2013. In Proceedings: 13th International Conference on Quality Software (QSIC), Nanjing, China, 29-30 July 2013, p. 268-275en_US
dc.identifier.urihttp://hdl.handle.net/10722/184863-
dc.descriptionCo-located with 13th International Conference on Quality Software (QSIC), Nanjing, China, 29-30 July 2013-
dc.description.abstractTest case selection is a prime process in the engineering of test harnesses. In particular, test case diversity is an important concept. In order to achieve an even spread of test cases across the input domain, Adaptive Random Testing (ART) was proposed such that the history of previously executed test cases are taken into consideration when selecting the next test case. This was achieved through various means such as best candidate selection, exclusion, partitioning, and diversity metrics. Empirical studies showed that ART algorithms make good use of the concept of even spreading and achieve 40 to 50% improvement in test effectiveness over random testing in revealing the first failure, which is close to the theoretical limit. However, the computational complexity of ART algorithms may be quadratic or higher, and hence efficiency is an issue when a large number of previously executed test cases are involved. This paper proposes an innovative divide-and-conquer approach to improve the efficiency of ART algorithms while maintaining their performance in effectiveness. Simulation studies have been conducted to gauge its efficiency against two most commonly used ART algorithms, namely, fixed size candidate set and restricted random testing. Initial experimental results show that the divide-and-conquer technique can provide much better efficiency while maintaining similar, or even better, effectiveness.-
dc.languageengen_US
dc.publisherIEEE Computer Society.-
dc.relation.ispartofProceedings: 13th International Conference on Quality Software (QSIC), Nanjing, China, 29-30 July 2013en_US
dc.subjectAdaptive random testing-
dc.subjectDivide and conquer-
dc.subjectEfficiency-
dc.subjectEffectiveness-
dc.subjectSoftware testing-
dc.titleThe ART of divide and conquer: an innovative approach to improving the efficiency of adaptive random testingen_US
dc.typeConference_Paperen_US
dc.identifier.emailTse, TH: thtse@cs.hku.hken_US
dc.identifier.authorityTse, TH=rp00546en_US
dc.description.naturepostprint-
dc.identifier.doi10.1109/QSIC.2013.19-
dc.identifier.hkuros215617en_US
dc.identifier.spage268-
dc.identifier.epage275-
dc.identifier.isiWOS:000335148200038-
dc.publisher.placeUnited States-
dc.customcontrol.immutableyiu 140328-

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