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In this article, the authors have proposed a co-change prediction model based upon the combination of structural code measures and dynamic revision history from change repository. Univariate analysis is applied to identify the useful measures in co-change identification. The proposed model is validated using eight open source software applications. The results are promising and indicate that they can be very beneficial in maintenance of software applications.<\/jats:p>","DOI":"10.4018\/jitr.2020040107","type":"journal-article","created":{"date-parts":[[2020,2,14]],"date-time":"2020-02-14T12:09:31Z","timestamp":1581682171000},"page":"110-128","source":"Crossref","is-referenced-by-count":4,"title":["Identification of Co-Changed Classes in Software Applications Using Software Quality Attributes"],"prefix":"10.4018","volume":"13","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, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8729-2293","authenticated-orcid":true,"given":"R. 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