{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,1,19]],"date-time":"2024-01-19T09:20:39Z","timestamp":1705656039982},"reference-count":47,"publisher":"World Scientific Pub Co Pte Lt","issue":"08","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Soft. Eng. Knowl. Eng."],"published-print":{"date-parts":[[2020,8]]},"abstract":"<jats:p>Fixed Sized Candidate Set (FSCS) is the first of a series of methods proposed to enhance the effectiveness of random testing (RT) referred to as Adaptive Random Testing methods or ARTs. Since its inception, test case generation overheads have been a major drawback to the success of ART. In FSCS, the bulk of this cost is embedded in distance computations between a set of randomly generated candidate test cases and previously executed but unsuccessful test cases. Consequently, FSCS is caught in a logical trap of probing the distances between every candidate and all executed test cases before the best candidate is determined. Using data mining, however, we discovered that about 50% of all valid test cases are encountered much earlier in the distance computations process but without any benefit of a hindsight, FSCS is unable to validate them; a wild goose chase. This paper then uses this information to propose a new strategy that predictively and proactively selects valid candidates anywhere during the distance computation process without vetting every candidate. Theoretical analysis, simulations and experimental studies conducted led to a similar conclusion: 25% of the distance computations are wasteful and can be discarded without any repercussion on effectiveness.<\/jats:p>","DOI":"10.1142\/s0218194020500308","type":"journal-article","created":{"date-parts":[[2020,10,15]],"date-time":"2020-10-15T09:52:14Z","timestamp":1602755534000},"page":"1169-1198","source":"Crossref","is-referenced-by-count":2,"title":["A Proactive Approach to Test Case Selection \u2014 An Efficient Implementation of Adaptive Random Testing"],"prefix":"10.1142","volume":"30","author":[{"given":"Michael","family":"Omari","sequence":"first","affiliation":[{"name":"School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, P. R. China"}]},{"given":"Jinfu","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, P. R. China"}]},{"given":"Robert","family":"French-Baidoo","sequence":"additional","affiliation":[{"name":"School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, P. R. China"}]},{"given":"Yunting","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, P. R. China"}]}],"member":"219","published-online":{"date-parts":[[2020,10,13]]},"reference":[{"key":"S0218194020500308BIB001","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1145\/2593882.2593885","volume-title":"Proc. Future of Software Engineering","author":"Orso A.","year":"2014"},{"key":"S0218194020500308BIB003","first-page":"320","volume-title":"Annual Asian Computing Science Conf.","author":"Chen T. Y.","year":"2004"},{"key":"S0218194020500308BIB004","first-page":"51","volume-title":"Proc. 9th Int. Conf. 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