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Association rule mining is a popular approach suggested in literature, but a major limitation of this approach is its inability to generate recommendations in case of new addition of classes. This article suggests the development of prediction model using learning techniques to overcome this limitation. The authors evaluate the performance of thirteen statistical, ML, and search-based techniques using eight open source software applications in this work. The findings of this study are promising and support the application of SBT and ML techniques for ripple effect identification.<\/p>","DOI":"10.4018\/ijossp.2020010103","type":"journal-article","created":{"date-parts":[[2020,3,13]],"date-time":"2020-03-13T15:58:42Z","timestamp":1584115122000},"page":"41-56","source":"Crossref","is-referenced-by-count":9,"title":["Ripple Effect Identification in Software Applications"],"prefix":"10.4018","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2811-6780","authenticated-orcid":true,"given":"Anushree","family":"Agrawal","sequence":"first","affiliation":[{"name":"Indira Gandhi Delhi Technical University for Women, Delhi, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8729-2293","authenticated-orcid":true,"given":"R.K.","family":"Singh","sequence":"additional","affiliation":[{"name":"Department of IT, Indira Gandhi Delhi Technical University for Women, Delhi, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"2432","reference":[{"key":"IJOSSP.2020010103-0","doi-asserted-by":"publisher","DOI":"10.1109\/ICSSIT.2018.8748406"},{"key":"IJOSSP.2020010103-1","doi-asserted-by":"crossref","unstructured":"Agrawal, A., & Singh, R. 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