{"title":"用统计分布模型分析软件演化过程","authors":"T. Tamai, Takako Nakatani","doi":"10.1145/512035.512063","DOIUrl":null,"url":null,"abstract":"Size data of software systems are constantly collected but so far there have been no studies of applying statistical distribution models to analyze and interpret those data. In this paper, we show that the negative binomial distribution fits well to the distribution of size data such as the number of methods per class and number of lines of code per method and can be effectively used to trace software evolution processes.","PeriodicalId":321820,"journal":{"name":"International Workshop on Principles of Software Evolution","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2002-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"21","resultStr":"{\"title\":\"Analysis of software evolution processes using statistical distribution Models\",\"authors\":\"T. Tamai, Takako Nakatani\",\"doi\":\"10.1145/512035.512063\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Size data of software systems are constantly collected but so far there have been no studies of applying statistical distribution models to analyze and interpret those data. In this paper, we show that the negative binomial distribution fits well to the distribution of size data such as the number of methods per class and number of lines of code per method and can be effectively used to trace software evolution processes.\",\"PeriodicalId\":321820,\"journal\":{\"name\":\"International Workshop on Principles of Software Evolution\",\"volume\":\"6 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2002-05-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"21\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Workshop on Principles of Software Evolution\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/512035.512063\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Workshop on Principles of Software Evolution","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/512035.512063","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Analysis of software evolution processes using statistical distribution Models
Size data of software systems are constantly collected but so far there have been no studies of applying statistical distribution models to analyze and interpret those data. In this paper, we show that the negative binomial distribution fits well to the distribution of size data such as the number of methods per class and number of lines of code per method and can be effectively used to trace software evolution processes.
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