{"title":"错误消息的频率分布","authors":"David Pritchard","doi":"10.1145/2846680.2846681","DOIUrl":null,"url":null,"abstract":"Which programming error messages are the most common? We investigate this question, motivated by writing error explanations for novices. We consider large data sets in Python and Java that include both syntax and run-time errors. In both data sets, after grouping essentially identical messages, the error message frequencies empirically resemble Zipf-Mandelbrot distributions. We use a maximum-likelihood approach to fit the distribution parameters. This gives one possible way to contrast languages or compilers quantitatively.","PeriodicalId":213941,"journal":{"name":"Proceedings of the 6th Workshop on Evaluation and Usability of Programming Languages and Tools","volume":"12 18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"27","resultStr":"{\"title\":\"Frequency distribution of error messages\",\"authors\":\"David Pritchard\",\"doi\":\"10.1145/2846680.2846681\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Which programming error messages are the most common? We investigate this question, motivated by writing error explanations for novices. We consider large data sets in Python and Java that include both syntax and run-time errors. In both data sets, after grouping essentially identical messages, the error message frequencies empirically resemble Zipf-Mandelbrot distributions. We use a maximum-likelihood approach to fit the distribution parameters. This gives one possible way to contrast languages or compilers quantitatively.\",\"PeriodicalId\":213941,\"journal\":{\"name\":\"Proceedings of the 6th Workshop on Evaluation and Usability of Programming Languages and Tools\",\"volume\":\"12 18 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-09-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"27\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 6th Workshop on Evaluation and Usability of Programming Languages and Tools\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2846680.2846681\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 6th Workshop on Evaluation and Usability of Programming Languages and Tools","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2846680.2846681","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Which programming error messages are the most common? We investigate this question, motivated by writing error explanations for novices. We consider large data sets in Python and Java that include both syntax and run-time errors. In both data sets, after grouping essentially identical messages, the error message frequencies empirically resemble Zipf-Mandelbrot distributions. We use a maximum-likelihood approach to fit the distribution parameters. This gives one possible way to contrast languages or compilers quantitatively.