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In this paper automated software test case generation with Radial Basis Function Neural Network (RBFNN) has been proposed and empirically validated with the help of a case study and compared with other techniques of soft computing. Experimental results show that RBFNN is one of the best technique for automated test case generation.<\/jats:p>","DOI":"10.1145\/2020976.2020992","type":"journal-article","created":{"date-parts":[[2011,10,11]],"date-time":"2011-10-11T14:29:02Z","timestamp":1318343342000},"page":"1-5","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["Radial basis function neural network based approach to test oracle"],"prefix":"10.1145","volume":"36","author":[{"given":"Om Prakash","family":"Sangwan","sequence":"first","affiliation":[{"name":"Gautam Buddha University, Greater Noida, Uttar Pradesh, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pradeep Kumar","family":"Bhatia","sequence":"additional","affiliation":[{"name":"GJU of Science &amp; Technology, Hisar, Haryana, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yogesh","family":"Singh","sequence":"additional","affiliation":[{"name":"M.S. 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