Towards Code Generation from BDD Test Case Specifications: A Vision
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 27 references that are in the map. First posted on arXiv 19 May 2023.
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References · 10
- 2017Attention Is All You Needcited by 60,361
- 2021Evaluating Large Language Models Trained on Codecited by 8,320
- 2020CodeBERT: A Pre-Trained Model for Programming and Natural Languagescited by 1,310
- 2021CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generationcited by 906
- 2020CodeBLEU: a Method for Automatic Evaluation of Code Synthesiscited by 479
- 2020IntelliCode Compose: Code Generation Using Transformercited by 227
- 2017A Survey of Machine Learning for Big Code and Naturalnesscited by 221
- 2020Code Prediction by Feeding Trees to Transformerscited by 70
- 2019Pythia: AI-assisted Code Completion Systemcited by 55
- 2020Empirical Study of Transformers for Source Codecited by 13
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