Entity Identifier: A Natural Text Parsing-based Framework For Entity Relation Extraction
This addresses a specific code generation task for developers working on object-oriented projects, but it is incremental as it builds on existing NLP and code generation methods.
The paper tackles the problem of generating object-oriented class code from requirements descriptions by using natural language processing to extract structured entity and relation information, resulting in a framework that automates CRUD class generation with a new dataset for evaluation.
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.