移动应用软件缺陷预测

2016 IEEE Symposium on Service-Oriented System Engineering (SOSE) Pub Date : 2016-03-01 DOI:10.1109/SOSE.2016.25

M. Ricky, Fredy Purnomo, B. Yulianto

{"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}

引用次数: 19

摘要

随着移动应用用户的不断增加，需要对移动应用是否存在缺陷进行检查。对比CART和Test Metrics，提出了一种SVM方法对应用中的类进行分类。结果表明，支持向量机方法在精度和准确度方面都有较好的效果。与CART和Test Metrics方法相比，SVM在移动应用缺陷预测中的准确率达到83%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Mobile Application Software Defect Prediction

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.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

2016 IEEE Symposium on Service-Oriented System Engineering (SOSE)

自引率

0.00%

发文量