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.
翻译:对于全堆网或应用程序开发,它需要一家软件公司或更具体地说一个有经验的开发者团队贡献其大部分时间和资源来设计网站,然后将其转换成代码。因此,当开发团队将UI的电线框架和数据库图状转换成实际工作系统时,其效率将大大降低。如果客户或开发者能够自动转换预先制作的全堆网设计,从而获得一个部分工作(如果不完全工作的话)代码,这将节省宝贵的资源并加快整个工作流程。在本文件中,我们提出了一个新颖的方法,即利用深层学习和计算机视野方法,从草图图像中生成骨骼代码。培训数据集是第一手草图中的低忠诚线框、数据库图和班图。方法由三个部分组成。首先,前端或界面元素的检测和从自定义的UI的电路框中提取。第二,通过Schema设计和最后,从类图中创建一个类文件。