自动GUI代码生成作为软件工程与数据挖掘的交叉研究领域，为加快软件开发、提高软件质量提供了新途径。已有研究成果存在GUI部件分类覆盖面窄、相似GUI搜索算法复杂度高、GUI代码未检查等问题，严重影响GUI代码生成质量。本项目围绕如何应用代码大数据仓库指导GUI代码的自动生成总结出一系列研究内容：1）部件分类层面，如何实现基于深度学习算法的部件类型全覆盖分类模型；2）相似GUI搜索层面，如何综合运用GUI部件的类型及语义信息，实现新的GUI搜索算法；3）代码质量保障层面，如何学习成功的人工GUI编程行为，建立GUI代码自动检查模型。该项目的成功实施将为GUI代码生成方法提供新的解决方案，进而提高软件质量，大幅缩减软件开发周期。

Automatic GUI code generation is a cross field of software engineering and data mining research, which provides a new way to accelerate software development and improve software quality. The existing research results have several limitations, such as narrow coverage of GUI component types, high complexity of similar GUI search algorithms, and unchecked GUI code, which seriously affect the quality of GUI code generation. This project focuses on applying the big code to guide the automatic GUI code generation, and the research contents include: 1) Studying the way of realizing the full coverage classification model of components based on deep learning algorithm, at the components classification level. 2) Studying the way of using the type and semantic information of GUI components to achieve a new GUI search algorithm, at the similar GUI search level. 3) Studying the way of learning successful artificial GUI programming behavior to establish an automatic inspection model for GUI code, at the code quality assurance level. The successful implementation of the project will provide a new solution for the code generation method, thereby improving the software quality and greatly reducing the software development cycle.