[{"data":1,"prerenderedAt":401},["ShallowReactive",2],{"publication-2022\u002Fvpp-art-an-efficient-implementation-of-fixed-size-candidate-set-adaptive-random-en":3,"publication-members":88},{"_path":4,"_dir":5,"_draft":6,"_partial":6,"_locale":7,"title":8,"description":7,"_hidden":6,"Random Testing Using Vantage Point Partitioning\" authors":9,"authors_orcid":15,"year":21,"doi":22,"openalex_id":23,"venue":24,"abstract_screenshot":25,"keywords":26,"body":40,"_type":81,"_id":82,"_source":83,"_file":84,"_stem":85,"_extension":86,"locale":87},"\u002Fpublications\u002F2022\u002Fvpp-art-an-efficient-implementation-of-fixed-size-candidate-set-adaptive-random","2022",false,"","VPP-ART: An Efficient Implementation of Fixed-Size-Candidate-Set Adaptiv",[10,11,12,13,14],"Huang, Rubing","Cui, Chenhui","Towey, Dave","Sun, Weifeng","Lian, Junlong",[16,17,18,19,20],"0000-0002-1769-6126","0009-0004-8746-316X","0000-0003-0877-4353","0000-0001-6013-1369","0009-0007-3167-1236",2022,"https:\u002F\u002Fdoi.org\u002F10.1109\u002Ftr.2022.3218602","W4206882053","IEEE Transactions on Reliability",null,[27,28,29,30,31,32,33,34,35,36,37,38,39],"Computer science","Random testing","Set (abstract data type)","Tree (set theory)","Artificial intelligence","Overhead (engineering)","Point (geometry)","Algorithm","Machine learning","Mathematics","Test case","Programming language","Combinatorics",{"type":41,"children":42,"toc":78},"root",[43],{"type":44,"tag":45,"props":46,"children":49},"element","italic",{"xmlns:mml":47,"xmlnsXLink":48},"http:\u002F\u002Fwww.w3.org\u002F1998\u002FMath\u002FMathML","http:\u002F\u002Fwww.w3.org\u002F1999\u002Fxlink",[50,53],{"type":51,"value":52},"text","\nAdaptive random testing\n\n (ART) is an enhancement of \n",{"type":44,"tag":45,"props":54,"children":55},{"xmlns:mml":47,"xmlnsXLink":48},[56,58],{"type":51,"value":57},"\nrandom testing\n\n (RT), and aims to improve the RT failure-detection effectiveness by distributing test cases more evenly in the input domain. Many ART algorithms have been proposed, with \n",{"type":44,"tag":45,"props":59,"children":60},{"xmlns:mml":47,"xmlnsXLink":48},[61,63],{"type":51,"value":62},"\nfixed-size-candidate-set\n\n ART (FSCS-ART) being one of the most effective and popular. FSCS-ART ensures high failure-detection effectiveness by selecting as the next test case the candidate farthest from previously executed test cases. Although FSCS-ART has good failure-detection effectiveness, it also faces some challenges, including heavy computational overheads. In this article, we propose an enhanced version of FSCS-ART, \n",{"type":44,"tag":45,"props":64,"children":65},{"xmlns:mml":47,"xmlnsXLink":48},[66,68],{"type":51,"value":67},"\nvantage point partitioning ART\n\n (VPP-ART). VPP-ART addresses the FSCS-ART computational overhead problem using VPP, while maintaining the failure-detection effectiveness. VPP-ART partitions the input domain space using a \n",{"type":44,"tag":45,"props":69,"children":70},{"xmlns:mml":47,"xmlnsXLink":48},[71,73],{"type":51,"value":72},"\nmodified vantage point tree\n\n (VP-tree) and finds the approximate nearest executed test cases of a candidate test case in the partitioned subdomains—thereby significantly reducing the time overheads compared with the searches required for FSCS-ART. To enable the FSCS-ART dynamic insertion process, we modify the traditional VP-tree to support dynamic data. The simulation results show that VPP-ART has a much lower time overhead compared to FSCS-ART, but also delivers similar (or better) failure-detection effectiveness, especially in the higher dimensional input domains. According to statistical analyses, VPP-ART can improve on the FSCS-ART failure-detection effectiveness by approximately 50–58%. VPP-ART also compares favorably with the \n",{"type":44,"tag":45,"props":74,"children":75},{"xmlns:mml":47,"xmlnsXLink":48},[76],{"type":51,"value":77},"\nKD-tree-enhanced fixed-size-candidate-set ART\n\n (KDFC-ART) algorithms (a series of enhanced ART algorithms based on the KD-tree). 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