{"title":"使用自然语言处理技术的NASA异常自动分类","authors":"D. Falessi, L. Layman","doi":"10.1109/ISSREW.2013.6688849","DOIUrl":null,"url":null,"abstract":"NASA anomaly databases are rich resources of software failure data in the field. These data are often captured in natural language that is not appropriate for trending or statistical analyses. This fast abstract describes a feasibility study of applying 60 natural language processing techniques for automatically classifying anomaly data to enable trend analyses.","PeriodicalId":332420,"journal":{"name":"2013 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Automated classification of NASA anomalies using natural language processing techniques\",\"authors\":\"D. Falessi, L. Layman\",\"doi\":\"10.1109/ISSREW.2013.6688849\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"NASA anomaly databases are rich resources of software failure data in the field. These data are often captured in natural language that is not appropriate for trending or statistical analyses. This fast abstract describes a feasibility study of applying 60 natural language processing techniques for automatically classifying anomaly data to enable trend analyses.\",\"PeriodicalId\":332420,\"journal\":{\"name\":\"2013 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)\",\"volume\":\"30 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-12-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISSREW.2013.6688849\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISSREW.2013.6688849","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Automated classification of NASA anomalies using natural language processing techniques
NASA anomaly databases are rich resources of software failure data in the field. These data are often captured in natural language that is not appropriate for trending or statistical analyses. This fast abstract describes a feasibility study of applying 60 natural language processing techniques for automatically classifying anomaly data to enable trend analyses.
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