Towards Code Generation from BDD Test Case Specifications: A Vision
This addresses software development efficiency for web developers, but appears incremental as it applies existing transformer methods to a new domain.
The paper tackles the problem of generating frontend component code for Angular by using behavior-driven development test specifications as input to a transformer-based model, aiming to reduce development time and improve software quality.
Automatic code generation has recently attracted large attention and is becoming more significant to the software development process. Solutions based on Machine Learning and Artificial Intelligence are being used to increase human and software efficiency in potent and innovative ways. In this paper, we aim to leverage these developments and introduce a novel approach to generating frontend component code for the popular Angular framework. We propose to do this using behavior-driven development test specifications as input to a transformer-based machine learning model. Our approach aims to drastically reduce the development time needed for web applications while potentially increasing software quality and introducing new research ideas toward automatic code generation.