An Empirical Study on Low Code Programming using Traditional vs Large Language Model Support
<|reference_start|>An Empirical Study on Low Code Programming using Traditional vs Large Language Model Support: Low-code programming (LCP) refers to programming using models at higher levels of abstraction, resulting in less manual and more efficient programming, and reduced learning effort for amateur developers. Many LCP tools have rapidly evolved and have benefited from the concepts of visual programming languages (VPLs) and programming by demonstration (PBD). With huge increase in interest in using large language models (LLMs) in software engineering, LLM-based LCP has began to become increasingly important. However, the technical principles and application scenarios of traditional approaches to LCP and LLM-based LCP are significantly different. Understanding these key differences and characteristics in the application of the two approaches to LCP by users is crucial for LCP providers in improving existing and developing new LCP tools, and in better assisting users in choosing the appropriate LCP technology. We conducted an empirical study of both traditional LCP and LLM-based LCP. We analyzed developers' discussions on Stack Overflow (SO) over the past three years and then explored the similarities and differences between traditional LCP and LLM-based LCP features and developer feedback. Our findings reveal that while traditional LCP and LLM-based LCP share common primary usage scenarios, they significantly differ in scope, limitations and usage throughout the software development lifecycle, particularly during the implementation phase. We also examine how LLMs impact and integrate with LCP, discussing the latest technological developments in LLM-based LCP, such as its integration with VPLs and the application of LLM Agents in software engineering.<|reference_end|>
@article{liu2024an,
    title={An Empirical Study on Low Code Programming using Traditional vs Large
  Language Model Support},
    author={Yongkun Liu, Jiachi Chen, Tingting Bi, John Grundy, Yanlin Wang,
  Jianxing Yu, Ting Chen, Yutian Tang, Zibin Zheng},
    journal={arXiv preprint arXiv:2402.01156},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.01156},
    primaryClass={cs.SE}
}