{"title":"利用统计主题模型挖掘GNOME bug报告的一致性","authors":"Erik J. Linstead, P. Baldi","doi":"10.1109/MSR.2009.5069486","DOIUrl":null,"url":null,"abstract":"We adapt Latent Dirichlet Allocation to the problem of mining bug reports in order to define a new information-theoretic measure of coherence. We then apply our technique to a snapshot of the GNOME Bugzilla database consisting of 431,863 bug reports for multiple software projects. In addition to providing an unsupervised means for modeling report content, our results indicate substantial promise in applying statistical text mining algorithms for estimating bug report quality. Complete results are available from our supplementary materials website at http://sourcerer.ics.uci.edu/msr2009/gnome_coherence.html.","PeriodicalId":413721,"journal":{"name":"2009 6th IEEE International Working Conference on Mining Software Repositories","volume":"101 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"40","resultStr":"{\"title\":\"Mining the coherence of GNOME bug reports with statistical topic models\",\"authors\":\"Erik J. Linstead, P. Baldi\",\"doi\":\"10.1109/MSR.2009.5069486\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We adapt Latent Dirichlet Allocation to the problem of mining bug reports in order to define a new information-theoretic measure of coherence. We then apply our technique to a snapshot of the GNOME Bugzilla database consisting of 431,863 bug reports for multiple software projects. In addition to providing an unsupervised means for modeling report content, our results indicate substantial promise in applying statistical text mining algorithms for estimating bug report quality. Complete results are available from our supplementary materials website at http://sourcerer.ics.uci.edu/msr2009/gnome_coherence.html.\",\"PeriodicalId\":413721,\"journal\":{\"name\":\"2009 6th IEEE International Working Conference on Mining Software Repositories\",\"volume\":\"101 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-05-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"40\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 6th IEEE International Working Conference on Mining Software Repositories\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MSR.2009.5069486\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 6th IEEE International Working Conference on Mining Software Repositories","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MSR.2009.5069486","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Mining the coherence of GNOME bug reports with statistical topic models
We adapt Latent Dirichlet Allocation to the problem of mining bug reports in order to define a new information-theoretic measure of coherence. We then apply our technique to a snapshot of the GNOME Bugzilla database consisting of 431,863 bug reports for multiple software projects. In addition to providing an unsupervised means for modeling report content, our results indicate substantial promise in applying statistical text mining algorithms for estimating bug report quality. Complete results are available from our supplementary materials website at http://sourcerer.ics.uci.edu/msr2009/gnome_coherence.html.
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