{"title":"移动应用软件缺陷预测","authors":"M. Ricky, Fredy Purnomo, B. Yulianto","doi":"10.1109/SOSE.2016.25","DOIUrl":null,"url":null,"abstract":"The increasing number of user of mobile application, it is needed to check mobile applications that contains defect or not. I proposed a SVM method in comparison with CART and Test Metrics to classify classes in application. It shows that SVM method has better result in terms of precision and accuracy. SVM accuracy reaches 83% compared with CART and Test Metrics method in mobile apps defect prediction.","PeriodicalId":153118,"journal":{"name":"2016 IEEE Symposium on Service-Oriented System Engineering (SOSE)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"19","resultStr":"{\"title\":\"Mobile Application Software Defect Prediction\",\"authors\":\"M. Ricky, Fredy Purnomo, B. Yulianto\",\"doi\":\"10.1109/SOSE.2016.25\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The increasing number of user of mobile application, it is needed to check mobile applications that contains defect or not. I proposed a SVM method in comparison with CART and Test Metrics to classify classes in application. It shows that SVM method has better result in terms of precision and accuracy. SVM accuracy reaches 83% compared with CART and Test Metrics method in mobile apps defect prediction.\",\"PeriodicalId\":153118,\"journal\":{\"name\":\"2016 IEEE Symposium on Service-Oriented System Engineering (SOSE)\",\"volume\":\"15 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"19\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 IEEE Symposium on Service-Oriented System Engineering (SOSE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SOSE.2016.25\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE Symposium on Service-Oriented System Engineering (SOSE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SOSE.2016.25","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The increasing number of user of mobile application, it is needed to check mobile applications that contains defect or not. I proposed a SVM method in comparison with CART and Test Metrics to classify classes in application. It shows that SVM method has better result in terms of precision and accuracy. SVM accuracy reaches 83% compared with CART and Test Metrics method in mobile apps defect prediction.
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