[{"data":1,"prerenderedAt":406},["ShallowReactive",2],{"publication-2024\u002Ftoward-cost-effective-adaptive-random-testing-an-approximate-nearest-neighbor-ap-en":3,"publication-members":93},{"_path":4,"_dir":5,"_draft":6,"_partial":6,"_locale":7,"title":8,"description":7,"_hidden":6,"Approach\" authors":9,"authors_orcid":16,"year":23,"doi":24,"openalex_id":25,"venue":26,"abstract_screenshot":27,"keywords":28,"body":35,"_type":86,"_id":87,"_source":88,"_file":89,"_stem":90,"_extension":91,"locale":92},"\u002Fpublications\u002F2024\u002Ftoward-cost-effective-adaptive-random-testing-an-approximate-nearest-neighbor-ap","2024",false,"","Toward Cost-Effective Adaptive Random Testing: An Approximate Nearest Neighbo",[10,11,12,13,14,15],"Huang, Rubing","Cui, Chenhui","Lian, Junlong","Towey, Dave","Sun, Weifeng","Chen, Haibo",[17,18,19,20,21,22],"0000-0002-1769-6126","0009-0004-8746-316X","0009-0007-3167-1236","0000-0003-0877-4353","0000-0001-6013-1369","0000-0002-3284-9143",2024,"https:\u002F\u002Fdoi.org\u002F10.1109\u002Ftse.2024.3379592","W4393058040","IEEE Transactions on Software Engineering",null,[29,30,31,32,33,34],"Computer science","k-nearest neighbors algorithm","Algorithm","Data mining","Theoretical computer science","Artificial intelligence",{"type":36,"children":37,"toc":83},"root",[38],{"type":39,"tag":40,"props":41,"children":44},"element","italic",{"xmlns:mml":42,"xmlnsXLink":43},"http:\u002F\u002Fwww.w3.org\u002F1998\u002FMath\u002FMathML","http:\u002F\u002Fwww.w3.org\u002F1999\u002Fxlink",[45,48],{"type":46,"value":47},"text","\nAdaptive Random Testing\n\n (ART) enhances the testing effectiveness (including fault-detection capability) of \n",{"type":39,"tag":40,"props":49,"children":50},{"xmlns:mml":42,"xmlnsXLink":43},[51,53],{"type":46,"value":52},"\nRandom Testing\n\n (RT) by increasing the diversity of the random test cases throughout the input domain. Many ART algorithms have been investigated such as \n",{"type":39,"tag":40,"props":54,"children":55},{"xmlns:mml":42,"xmlnsXLink":43},[56,58],{"type":46,"value":57},"\nFixed-Size-Candidate-Set ART\n\n (FSCS) and \n",{"type":39,"tag":40,"props":59,"children":60},{"xmlns:mml":42,"xmlnsXLink":43},[61,63],{"type":46,"value":62},"\nRestricted Random Testing\n\n (RRT), and have been widely used in many practical applications. Despite its popularity, ART suffers from the problem of high computational costs during test-case generation, especially as the number of test cases increases. Although several strategies have been proposed to enhance the ART testing efficiency, such as the \n",{"type":39,"tag":40,"props":64,"children":65},{"xmlns:mml":42,"xmlnsXLink":43},[66,68],{"type":46,"value":67},"\nforgetting strategy\n\n and the \n",{"type":39,"tag":40,"props":69,"children":70},{"xmlns:mml":42,"xmlnsXLink":43},[71,73],{"type":46,"value":72},"\nk-dimensional tree strategy\n\n , these algorithms still face some challenges, including: (1) Although these algorithms can reduce the computation time, their execution costs are still very high, especially when the number of test cases is large; and (2) To achieve low computational costs, they may sacrifice some fault-detection capability. In this paper, we propose an approach based on \n",{"type":39,"tag":40,"props":74,"children":75},{"xmlns:mml":42,"xmlnsXLink":43},[76,78],{"type":46,"value":77},"\nApproximate Nearest Neighbors\n\n (ANNs), called \n",{"type":39,"tag":40,"props":79,"children":80},{"xmlns:mml":42,"xmlnsXLink":43},[81],{"type":46,"value":82},"\nLocality-Sensitive Hashing ART\n\n (LSH-ART). When calculating distances among different test inputs, LSH-ART identifies the approximate (not necessarily exact) nearest neighbors for candidates in an efficient way. 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