Towards Code Generation from BDD Test Case Specifications: A Vision

Leon Chemnitz, David Reichenbach, Hani Aldebes, Mariam Naveed, Krishna Narasimhan, Mira Mezini

Abstract: 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; however, we do not provide any proof-of-concept solution in this work. 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.