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Experimenting a New Programming Practice with LLMs
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The recent development on large language models makes automatically constructing small programs possible. It thus has the potential to free software engineers from low-level coding and allow us to focus on the perhaps more interesting parts of software development, such as requirement engineering and system testing. In this project, we develop a prototype named AISD (AI-aided Software Development), which is capable of taking high-level (potentially vague) user requirements as inputs, generates detailed use cases, prototype system designs, and subsequently system implementation. Different from existing attempts, AISD is designed to keep the user in the loop, i.e., by repeatedly taking user feedback on use cases, high-level system designs, and prototype implementations through system testing. AISD has been evaluated with a novel benchmark of non-trivial software projects. The experimental results suggest that it might be possible to imagine a future where software engineering is reduced to requirement engineering and system testing only.
Forward citations
Cited by 2 Pith papers
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Towards Iterative End-to-End Software Development: A Feature-Driven Multi-Agent Framework
EvoDev introduces an iterative feature-driven framework with a DAG-based Feature Map for context propagation that improves LLM agent performance on end-to-end software development tasks by 56.8% over the best baseline.
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iReDev: A Knowledge-Driven Multi-Agent Framework for Intelligent Requirements Development
A knowledge-driven, event-triggered multi-agent framework called iReDev generates software requirements artifacts that outperform zero-shot prompting, MetaGPT, and Elicitron on ten small projects.
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