{"title":"遗传程序在预测变化和缺陷方面有多好?","authors":"C. Marinescu","doi":"10.1109/SYNASC.2014.78","DOIUrl":null,"url":null,"abstract":"One of the main problems practitioners have to deal with is the identification of change and defect proneness of source code entities (e.g., Classes). During the last years a lot of techniques have been employed for predicting change and defect proneness of classes. In this paper we study the capabilities of Genetic Programming for performing the addressed problem by measuring the precision and recall of the obtained predictions.","PeriodicalId":150575,"journal":{"name":"2014 16th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2014-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"16","resultStr":"{\"title\":\"How Good Is Genetic Programming at Predicting Changes and Defects?\",\"authors\":\"C. Marinescu\",\"doi\":\"10.1109/SYNASC.2014.78\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"One of the main problems practitioners have to deal with is the identification of change and defect proneness of source code entities (e.g., Classes). During the last years a lot of techniques have been employed for predicting change and defect proneness of classes. In this paper we study the capabilities of Genetic Programming for performing the addressed problem by measuring the precision and recall of the obtained predictions.\",\"PeriodicalId\":150575,\"journal\":{\"name\":\"2014 16th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"16\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 16th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SYNASC.2014.78\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 16th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SYNASC.2014.78","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
How Good Is Genetic Programming at Predicting Changes and Defects?
One of the main problems practitioners have to deal with is the identification of change and defect proneness of source code entities (e.g., Classes). During the last years a lot of techniques have been employed for predicting change and defect proneness of classes. In this paper we study the capabilities of Genetic Programming for performing the addressed problem by measuring the precision and recall of the obtained predictions.
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