Source Code Generation-based on NLP and Ontology
alokla, A., Gad, W., Aref, M., Salem, A. (2022). Source Code Generation-based on NLP and Ontology. EKB Journal Management System, 22(4), 1-12. doi: 10.21608/ijicis.2022.117905.1160
anas hamid alokla; Walaa Gad; M Aref; Abdel-Badeeh M. Salem. "Source Code Generation-based on NLP and Ontology". EKB Journal Management System, 22, 4, 2022, 1-12. doi: 10.21608/ijicis.2022.117905.1160
alokla, A., Gad, W., Aref, M., Salem, A. (2022). 'Source Code Generation-based on NLP and Ontology', EKB Journal Management System, 22(4), pp. 1-12. doi: 10.21608/ijicis.2022.117905.1160
alokla, A., Gad, W., Aref, M., Salem, A. Source Code Generation-based on NLP and Ontology. EKB Journal Management System, 2022; 22(4): 1-12. doi: 10.21608/ijicis.2022.117905.1160

Source Code Generation-based on NLP and Ontology

International Journal of Intelligent Computing and Information Sciences
Article 1, Volume 22, Issue 4, December 2022, Page 1-12  XML PDF (462.44 K)
Document Type: Original Article
DOI: 10.21608/ijicis.2022.117905.1160
View on SCiNiTO View on SCiNiTO
Authors
anas hamid alokla email 1; Walaa Gadorcid 2; M Areforcid 3; Abdel-Badeeh M. Salemorcid 4
1computer science / FCIS - Ain Shams University
2Information Systems Department, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt
3Department Computer Science, Faculty of Computer and Information Sciences,Ain Shams University, Cairo, Egypt.
4Computer Sciece Department, Faculty of Computer and Information Sciences, Ain Shams University
Abstract
Generating source code is necessary especially as software evolves in complexity and demand. Finding a mechanism to generate the source code according to the requirements will save time for developers at the stage of development of the software. In this paper, a mechanism is proposed to generate the source code based on the database schema and user requirements (user story). This model contains three layers: The first layer is to analyze each of the database schema, extract the relationships between the tables, determine the meanings of the fields and analyze the user’s story to find the functions performed by each role of the software users. The second layer is deducing new functions based on what was mentioned in the first layer and extracting the knowledge that contains the solutions to the problems that are inferred. The knowledge bases used are WordNet and Backend Ontology built from scratch. In the third Layer, the solutions are converted to source code based on templates extracted from the knowledge and configured, that is applied to the templates. The model showed success in generating the source code, generating PHP source code for a site that is tested and generated seventy percent of what was required to be written by programmers.
Keywords
Automatic Programming; source code genration; Natural Language Processing; Ontological Engneering; knowledge Engineering
Statistics
Article View: 344
PDF Download: 313