Entity Identifier: A Natural Text Parsing-based Framework For Entity Relation Extraction

TL;DR

This paper presents a framework that uses natural language processing to extract structured entity and relation information from requirements descriptions, automating CRUD class code generation in object-oriented programming.

Contribution

It introduces a novel pipeline and an 'Entity Tree' representation for extracting and modeling entity-relation information from natural language requirements.

Findings

Effective extraction of entity-relation information demonstrated

Automated generation of CRUD class code shown to be feasible

New dataset created for evaluation purposes

Abstract

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.