{"title":"使用机器学习技术进行软件缺陷预测","authors":"G. Cauvery, D. DhinaSuresh","doi":"10.46632/daai/3/2/7","DOIUrl":null,"url":null,"abstract":"Software defect prediction provides development groups with observable outcomes while contributing to industrial results and development faults predicting defective code areas can help developers identify bugs and organize their test activities. The percentage of classification providing the proper prediction is essential for early identification. Moreover, software- defected data sets are supported and at least partially recognized due to their enormous dimension.","PeriodicalId":226827,"journal":{"name":"Data Analytics and Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Software Defect Prediction Using Machine Learning Techniques\",\"authors\":\"G. Cauvery, D. DhinaSuresh\",\"doi\":\"10.46632/daai/3/2/7\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Software defect prediction provides development groups with observable outcomes while contributing to industrial results and development faults predicting defective code areas can help developers identify bugs and organize their test activities. The percentage of classification providing the proper prediction is essential for early identification. Moreover, software- defected data sets are supported and at least partially recognized due to their enormous dimension.\",\"PeriodicalId\":226827,\"journal\":{\"name\":\"Data Analytics and Artificial Intelligence\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Data Analytics and Artificial Intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.46632/daai/3/2/7\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Data Analytics and Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.46632/daai/3/2/7","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Software Defect Prediction Using Machine Learning Techniques
Software defect prediction provides development groups with observable outcomes while contributing to industrial results and development faults predicting defective code areas can help developers identify bugs and organize their test activities. The percentage of classification providing the proper prediction is essential for early identification. Moreover, software- defected data sets are supported and at least partially recognized due to their enormous dimension.
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