An Empirical Study on Low Code Programming using Traditional vs Large Language Model Support
CoRR(2024)
摘要
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.
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