We present Text2App -- a framework that allows users to create functional Android applications from natural language specifications. The conventional method of source code generation tries to generate source code directly, which is impractical for creating complex software. We overcome this limitation by transforming natural language into an abstract intermediate formal language representing an application with a substantially smaller number of tokens. The intermediate formal representation is then compiled into target source codes. This abstraction of programming details allows seq2seq networks to learn complex application structures with less overhead. In order to train sequence models, we introduce a data synthesis method grounded in a human survey. We demonstrate that Text2App generalizes well to unseen combination of app components and it is capable of handling noisy natural language instructions. We explore the possibility of creating applications from highly abstract instructions by coupling our system with GPT-3 -- a large pretrained language model. The source code, a ready-to-run demo notebook, and a demo video are publicly available at \url{http://text2app.github.io}.

Text2App是一个可将自然语言转化为Android应用程序的框架，采用抽象中间形式语言来实现代码生成，结合人类调查的数据合成方法对序列模型进行训练，在GPT-3的帮助下，它能够从高度抽象的指令中创建应用程序，并且具有普遍适用的能力和使用自然语言指令的能力。

Text2App: 从文本描述创建Android应用的框架