{"title":"引导代码合成使用深度神经网络","authors":"Carol V. Alexandru","doi":"10.1145/2950290.2983951","DOIUrl":null,"url":null,"abstract":"Can we teach computers how to program? Recent advances in neural network research reveal that certain neural networks are able not only to learn the syntax, grammar and semantics of arbitrary character sequences, but also synthesize new samples `in the style of' the original training data. We explore the adaptation of these techniques to code classification, comprehension and completion.","PeriodicalId":20532,"journal":{"name":"Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering","volume":"38 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"Guided code synthesis using deep neural networks\",\"authors\":\"Carol V. Alexandru\",\"doi\":\"10.1145/2950290.2983951\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Can we teach computers how to program? Recent advances in neural network research reveal that certain neural networks are able not only to learn the syntax, grammar and semantics of arbitrary character sequences, but also synthesize new samples `in the style of' the original training data. We explore the adaptation of these techniques to code classification, comprehension and completion.\",\"PeriodicalId\":20532,\"journal\":{\"name\":\"Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering\",\"volume\":\"38 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2950290.2983951\",\"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 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2950290.2983951","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Can we teach computers how to program? Recent advances in neural network research reveal that certain neural networks are able not only to learn the syntax, grammar and semantics of arbitrary character sequences, but also synthesize new samples `in the style of' the original training data. We explore the adaptation of these techniques to code classification, comprehension and completion.
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