{"title":"下一个句法单元代码自动补全及应用","authors":"A. Nguyen, Aashish Yadavally, T. Nguyen","doi":"10.1145/3551349.3559544","DOIUrl":null,"url":null,"abstract":"Code completion is an important feature in an IDE to improve developers’ productivity. Existing code completion approaches focus on completing the current code token, next token or statement, or code pattern. We propose AstCC, a code completion approach to suggest the next syntactic unit via an AST-based statistical language model. AstCC learns from a large code corpus to derive the next AST subtree representing a syntactic unit, and then fills in the template with the concrete variables from the current program scope. Our empirical evaluation shows that AstCC can correctly suggest the next syntactic unit in 33% of the cases, and in 62% of the cases, it correctly suggests within five candidates. We will also explain the potential applications of AstCC in automated program repair, automated test case generation, and syntactic pattern mining.","PeriodicalId":197939,"journal":{"name":"Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering","volume":"57 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Next Syntactic-Unit Code Completion and Applications\",\"authors\":\"A. Nguyen, Aashish Yadavally, T. Nguyen\",\"doi\":\"10.1145/3551349.3559544\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Code completion is an important feature in an IDE to improve developers’ productivity. Existing code completion approaches focus on completing the current code token, next token or statement, or code pattern. We propose AstCC, a code completion approach to suggest the next syntactic unit via an AST-based statistical language model. AstCC learns from a large code corpus to derive the next AST subtree representing a syntactic unit, and then fills in the template with the concrete variables from the current program scope. Our empirical evaluation shows that AstCC can correctly suggest the next syntactic unit in 33% of the cases, and in 62% of the cases, it correctly suggests within five candidates. We will also explain the potential applications of AstCC in automated program repair, automated test case generation, and syntactic pattern mining.\",\"PeriodicalId\":197939,\"journal\":{\"name\":\"Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering\",\"volume\":\"57 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-10-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3551349.3559544\",\"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 37th IEEE/ACM International Conference on Automated Software Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3551349.3559544","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Next Syntactic-Unit Code Completion and Applications
Code completion is an important feature in an IDE to improve developers’ productivity. Existing code completion approaches focus on completing the current code token, next token or statement, or code pattern. We propose AstCC, a code completion approach to suggest the next syntactic unit via an AST-based statistical language model. AstCC learns from a large code corpus to derive the next AST subtree representing a syntactic unit, and then fills in the template with the concrete variables from the current program scope. Our empirical evaluation shows that AstCC can correctly suggest the next syntactic unit in 33% of the cases, and in 62% of the cases, it correctly suggests within five candidates. We will also explain the potential applications of AstCC in automated program repair, automated test case generation, and syntactic pattern mining.