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Conventional SDP approaches use oversampling techniques, such as synthetic oversampling, to tackle the problem of imbalanced data. However, these methods merely synthesize new instances based on traditional code features without considering actual defects at the code level. To address the issue of data imbalance while preserving semantic features of code samples, a mutation\u2010based data augmentation approach in SDP is proposed. The method utilizes the mutation operator to generate mutants that mutate nondefective instances and create new defective instances. Six projects from the PROMISE dataset are used to evaluate the approach, employing four traditional and two deep classifiers. The experimental results demonstrate the effectiveness of this method in improving defect prediction performance for both traditional and deep classifiers compared with other data augmentation methods.<\/jats:p>","DOI":"10.1002\/smr.2634","type":"journal-article","created":{"date-parts":[[2023,11,7]],"date-time":"2023-11-07T01:04:43Z","timestamp":1699319083000},"update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Mutation\u2010based data augmentation for software defect prediction"],"prefix":"10.1002","volume":"36","author":[{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0009-0003-6545-795X","authenticated-orcid":false,"given":"Rui","family":"Mao","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology Soochow University Suzhou China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology Soochow University Suzhou China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-8667-0456","authenticated-orcid":false,"given":"Xiaofang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology Soochow University Suzhou China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2023,11,6]]},"reference":[{"issue":"3","key":"e_1_2_11_2_1","article-title":"Handbook of software reliability engineering","volume":"18","author":"Lyu MR","year":"1996","journal-title":"Softw IEEE"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2008.35"},{"key":"e_1_2_11_4_1","doi-asserted-by":"crossref","unstructured":"NamJ KimS.CLAMI: defect prediction on unlabeled datasets (t). 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