Advancements and Prospects in Machine Learning-Driven Code Generation and Completion

Menghao Hu

2024

Abstract

Code generation and completion technologies have become important tools for enhancing development efficiency in modern software development, especially with recent breakthroughs in large-scale pre-trained language models, bringing new development opportunities to the field. With the advancement of machine learning technologies, particularly large language models, users can now generate and complete code through textual interaction, presenting new opportunities in this field. This paper reviews code generation and completion technologies based on machine learning and large-scale pre-trained models, analyzes the advantages and disadvantages of these methods, and discusses their performance and challenges in practical applications. The research shows that although large models perform well in semantic understanding and cross-language code generation, further optimization is needed regarding computational resource consumption and evaluation standards. Finally, this paper explores the future research directions of code generation technologies, providing references for improving the efficiency of large models, establishing unified evaluation standards, and enhancing the practical usability of generated code.

Paper Citation

in Harvard Style

Hu M. (2024). Advancements and Prospects in Machine Learning-Driven Code Generation and Completion. In Proceedings of the 1st International Conference on Modern Logistics and Supply Chain Management - Volume 1: MLSCM; ISBN 978-989-758-738-2, SciTePress, pages 392-396. DOI: 10.5220/0013332700004558

in Bibtex Style

@conference{mlscm24,
author={Menghao Hu},
title={Advancements and Prospects in Machine Learning-Driven Code Generation and Completion},
booktitle={Proceedings of the 1st International Conference on Modern Logistics and Supply Chain Management - Volume 1: MLSCM},
year={2024},
pages={392-396},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013332700004558},
isbn={978-989-758-738-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Modern Logistics and Supply Chain Management - Volume 1: MLSCM
TI - Advancements and Prospects in Machine Learning-Driven Code Generation and Completion
SN - 978-989-758-738-2
AU - Hu M.
PY - 2024
SP - 392
EP - 396
DO - 10.5220/0013332700004558
PB - SciTePress