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Experimenting a New Programming Practice with LLMs

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arxiv 2401.01062 v1 pith:LXTR3X5E submitted 2024-01-02 cs.SE

classification cs.SE
keywords systemsoftwareaisddevelopmentengineeringprototypetestinguser
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 4 citations worldwide. Full citation record

  1. Towards Iterative End-to-End Software Development: A Feature-Driven Multi-Agent Framework

    cs.SE 2025-11 unverdicted novelty 6.0 of 10

    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.

  2. iReDev: A Knowledge-Driven Multi-Agent Framework for Intelligent Requirements Development

    cs.SE 2025-07 conditional novelty 6.0 of 10

    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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