Code Generation by Example

Kevin Lano, Qiaomu Xue

2022

Abstract

Code generation is a key technique for model-driven engineering approaches of software construction. Code generation enables the synthesis of applications in executable programming languages from high-level specifications in UML or a domain-specific language. Specialised code-generation languages and tools have been defined, such as Epsilon EGL and Acceleo, however the task of writing a code generator remains a substantial undertaking, requiring a high degree of expertise in both the source and target languages, and in the code-generation language. In this paper we show how symbolic machine learning techniques can be used to reduce the time and effort for developing code generators. We apply the techniques to the development of a UML-to-Java code generator.

Paper Citation

in Harvard Style

Lano K. and Xue Q. (2022). Code Generation by Example. In Proceedings of the 10th International Conference on Model-Driven Engineering and Software Development - Volume 1: MODELSWARD, ISBN 978-989-758-550-0, pages 84-92. DOI: 10.5220/0010973600003119

in Bibtex Style

@conference{modelsward22,
author={Kevin Lano and Qiaomu Xue},
title={Code Generation by Example},
booktitle={Proceedings of the 10th International Conference on Model-Driven Engineering and Software Development - Volume 1: MODELSWARD,},
year={2022},
pages={84-92},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010973600003119},
isbn={978-989-758-550-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Conference on Model-Driven Engineering and Software Development - Volume 1: MODELSWARD,
TI - Code Generation by Example
SN - 978-989-758-550-0
AU - Lano K.
AU - Xue Q.
PY - 2022
SP - 84
EP - 92
DO - 10.5220/0010973600003119