The field of programming has a diversity of paradigms that are used according to the working framework. While current neural code generation methods are able to learn and generate code directly from text, we believe that this approach is not optimal for certain code tasks, particularly the generation of classes in an object-oriented project. Specifically, we use natural language processing techniques to extract structured information from requirements descriptions, in order to automate the generation of CRUD (Create, Read, Update, Delete) class code. To facilitate this process, we introduce a pipeline for extracting entity and relation information, as well as a representation called an "Entity Tree" to model this information. We also create a dataset to evaluate the effectiveness of our approach.

翻译：编程领域存在多种范式，可根据工作框架灵活选用。尽管当前神经代码生成方法能够直接从文本中学习并生成代码，但我们认为该方法对特定代码任务并非最优解，尤其是在面向对象项目中生成类代码的场景。具体而言，我们运用自然语言处理技术从需求描述中提取结构化信息，旨在自动化生成CRUD（增删改查）类代码。为推进该流程，我们引入了一套实体关系信息抽取管道，并提出名为"实体树"的表示方法对信息进行建模。此外，我们还构建了数据集以评估该方法的有效性。