2022/11/26 by Somoy Subandhu Barua, Barua, Somoy Subandhu, Imam Mohammad Zulkarnain +7 · 1 citation
Computer Science · #68T07 (Primary) #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #D.2.1 #D.2.2 #FOS: Computer and information sciences #I.2.10 #I.2.2 #I.2.5 #I.4.0 #I.4.9 #I.7.0 #Neural and Evolutionary Computing (cs.NE) #Software Engineering (cs.SE) #Software Engineering Research #Software Engineering Techniques and Practices #Web Data Mining and Analysis
paper · pdf · doi:10.48550/arxiv.2211.14607
openalex publication_date 2022/11/26 · openalex created_date 2022/12/10 · openalex updated_date 2026/07/28
For a full-stack web or app development, it requires a software firm or more specifically a team of experienced developers to contribute a large portion of their time and resources to design the website and then convert it to code. As a result, the efficiency of the development team is significantly reduced when it comes to converting UI wireframes and database schemas into an actual working system. It would save valuable resources and fasten the overall workflow if the clients or developers can automate this process of converting the pre-made full-stack website design to get a partially working if not fully working code. In this paper, we present a novel approach of generating the skeleton code from sketched images using Deep Learning and Computer Vision approaches. The dataset for training are first-hand sketched images of low fidelity wireframes, database schemas and class diagrams. The approach consists of three parts. First, the front-end or UI elements detection and extraction from custom-made UI wireframes. Second, individual database table creation from schema designs and lastly, creating a class file from class diagrams.