{"title":"源代码生成的本体工程","authors":"Anas Alokla, Walaa K. Gad, M. Aref, A. M. Salem","doi":"10.54623/fue.fcij.4.2.1","DOIUrl":null,"url":null,"abstract":"Source Code Generation (SCG) is the sub-domain of the Automatic Programming (AP) that helps programmers to program using high-level abstraction. Recently, many researchers investigated many techniques to access SCG. The problem is to use the appropriate technique to generate the source code due to its purposes and the inputs. This paper introduces a review and an analysis related SCG techniques. Moreover, comparisons are presented for: techniques mapping, Natural Language Processing (NLP), knowledgebase, ontology, Specification Configuration Template (SCT) model and deep learning.","PeriodicalId":100561,"journal":{"name":"Future Computing and Informatics Journal","volume":"70 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2020-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Ontological Engineering For Source Code Generation\",\"authors\":\"Anas Alokla, Walaa K. Gad, M. Aref, A. M. Salem\",\"doi\":\"10.54623/fue.fcij.4.2.1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Source Code Generation (SCG) is the sub-domain of the Automatic Programming (AP) that helps programmers to program using high-level abstraction. Recently, many researchers investigated many techniques to access SCG. The problem is to use the appropriate technique to generate the source code due to its purposes and the inputs. This paper introduces a review and an analysis related SCG techniques. Moreover, comparisons are presented for: techniques mapping, Natural Language Processing (NLP), knowledgebase, ontology, Specification Configuration Template (SCT) model and deep learning.\",\"PeriodicalId\":100561,\"journal\":{\"name\":\"Future Computing and Informatics Journal\",\"volume\":\"70 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-09-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Future Computing and Informatics Journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.54623/fue.fcij.4.2.1\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Future Computing and Informatics Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.54623/fue.fcij.4.2.1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Ontological Engineering For Source Code Generation
Source Code Generation (SCG) is the sub-domain of the Automatic Programming (AP) that helps programmers to program using high-level abstraction. Recently, many researchers investigated many techniques to access SCG. The problem is to use the appropriate technique to generate the source code due to its purposes and the inputs. This paper introduces a review and an analysis related SCG techniques. Moreover, comparisons are presented for: techniques mapping, Natural Language Processing (NLP), knowledgebase, ontology, Specification Configuration Template (SCT) model and deep learning.