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Article: KDFC-ART: a KD-tree approach to enhancing Fixed-size-Candidate-set Adaptive Random Testing

TitleKDFC-ART: a KD-tree approach to enhancing Fixed-size-Candidate-set Adaptive Random Testing
Authors
KeywordsPower capacitors
Testing
Subspace constraints
Software
Strips
Issue Date2019
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp/?punumber=24
Citation
IEEE Transactions on Reliability, 2019, Epub How to Cite?
AbstractAdaptive random testing (ART) was developed as an enhanced version of random testing to increase the effectiveness of detecting failures in programs by spreading the test cases evenly over the input space. However, heavy computation may be incurred. In this paper, three enhanced algorithms for fixedsize-candidate-set ART (FSCS-ART) are proposed based on the k-dimensional tree (KD-tree) structure. The first algorithm Naive-KDFC constructs a KD-tree by splitting the input space with respect to every dimension successively in a round-robin fashion. The second algorithm SemiBal-KDFC improves the balance of the KD-tree by prioritizing the splitting according to the spread in each dimension. In order to control the number of traversed nodes in backtracking, the third algorithm LimBal-KDFC introduces an upper bound for the nodes involved. Simulation and empirical studies have been conducted to investigate the efficiency and effectiveness of the three algorithms. The experimental results show that these algorithms significantly reduce the computation time of the original FSCS-ART for low dimensions and for the case of high dimensions with low failure rates. The efficiency of SemiBal-KDFC is better than that of Naive-KDFC when the dimension is no more than 8, but LimBal-KDFC is the most efficient of all three. Although limited backtracking leads only to an approximate nearest neighbor in LimBal-KDFC, its failure-detection effectiveness is, in fact, better than FSCS-ART in high-dimensional input spaces and has no significant deterioration in low-dimensional spaces.
Persistent Identifierhttp://hdl.handle.net/10722/271351
ISSN
2017 Impact Factor: 2.729
2015 SCImago Journal Rankings: 1.930

 

DC FieldValueLanguage
dc.contributor.authorMao, C-
dc.contributor.authorZhan, X-
dc.contributor.authorTse, TH-
dc.contributor.authorChen, TY-
dc.date.accessioned2019-06-24T01:08:11Z-
dc.date.available2019-06-24T01:08:11Z-
dc.date.issued2019-
dc.identifier.citationIEEE Transactions on Reliability, 2019, Epub-
dc.identifier.issn0018-9529-
dc.identifier.urihttp://hdl.handle.net/10722/271351-
dc.description.abstractAdaptive random testing (ART) was developed as an enhanced version of random testing to increase the effectiveness of detecting failures in programs by spreading the test cases evenly over the input space. However, heavy computation may be incurred. In this paper, three enhanced algorithms for fixedsize-candidate-set ART (FSCS-ART) are proposed based on the k-dimensional tree (KD-tree) structure. The first algorithm Naive-KDFC constructs a KD-tree by splitting the input space with respect to every dimension successively in a round-robin fashion. The second algorithm SemiBal-KDFC improves the balance of the KD-tree by prioritizing the splitting according to the spread in each dimension. In order to control the number of traversed nodes in backtracking, the third algorithm LimBal-KDFC introduces an upper bound for the nodes involved. Simulation and empirical studies have been conducted to investigate the efficiency and effectiveness of the three algorithms. The experimental results show that these algorithms significantly reduce the computation time of the original FSCS-ART for low dimensions and for the case of high dimensions with low failure rates. The efficiency of SemiBal-KDFC is better than that of Naive-KDFC when the dimension is no more than 8, but LimBal-KDFC is the most efficient of all three. Although limited backtracking leads only to an approximate nearest neighbor in LimBal-KDFC, its failure-detection effectiveness is, in fact, better than FSCS-ART in high-dimensional input spaces and has no significant deterioration in low-dimensional spaces.-
dc.languageeng-
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp/?punumber=24-
dc.relation.ispartofIEEE Transactions on Reliability-
dc.rightsIEEE Transactions on Reliability. Copyright © IEEE.-
dc.rights©2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.subjectPower capacitors-
dc.subjectTesting-
dc.subjectSubspace constraints-
dc.subjectSoftware-
dc.subjectStrips-
dc.titleKDFC-ART: a KD-tree approach to enhancing Fixed-size-Candidate-set Adaptive Random Testing-
dc.typeArticle-
dc.identifier.emailTse, TH: thtse@cs.hku.hk-
dc.identifier.authorityTse, TH=rp00546-
dc.description.naturepostprint-
dc.identifier.doi10.1109/TR.2019.2892230-
dc.identifier.hkuros298101-
dc.publisher.placeUnited States-

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