mlsanityData to 7 Oct 2026

Experimenting a New Programming Practice with LLMs

arXiv 2401.01062 ↗First posted 2 Jan 2024cs.SEcited by 17

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Jan 2024.

Builds on

No reference list yet.

Built on by

Cited by year

Citing papers per year: 2024: 5; 2025: 12.

17 papers.

Sort:

By area:

  1. 2024Large Language Model-Based Agents for Software Engineering: A Surveycited by 173
  2. 2024LLM-Based Multi-Agent Systems for Software Engineering: Literature Review, Vision and the Road Aheadcited by 155
  3. 2024From LLMs to LLM-based Agents for Software Engineering: A Survey of Current, Challenges and Futurecited by 85
  4. 2025ProjectEval: A Benchmark for Programming Agents Automated Evaluation on Project-Level Code Generationcited by 26
  5. 2025From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligencecited by 13
  6. 2024LLMs: A Game-Changer for Software Engineers?cited by 11
  7. 2025A Comprehensive Empirical Evaluation of Agent Frameworks on Code-centric Software Engineering Taskscited by 7
  8. 2025Shadows in the Code: Exploring the Risks and Defenses of LLM-based Multi-Agent Software Development Systemscited by 7
  9. 2025Towards Realistic Project-Level Code Generation via Multi-Agent Collaboration and Semantic Architecture Modelingcited by 6
  10. 2025Towards Advancing Code Generation with Large Language Models: A Research Roadmapcited by 5
  11. 2025Benchmarking AI Models in Software Engineering: A Review, Search Tool, and Unified Approach for Elevating Benchmark Qualitycited by 3
  12. 2025Designing LLM-based Multi-Agent Systems for Software Engineering Tasks: Quality Attributes, Design Patterns and Rationalecited by 3
5 more
  1. 2024The importance of visual modelling languages in generative software engineeringcited by 1
  2. 2025TREAT: A Code LLMs Trustworthiness / Reliability Evaluation and Testing Frameworkcited by 1
  3. 2025Towards Iterative End-to-End Software Development: A Feature-Driven Multi-Agent Frameworkcited by 1
  4. 2025DevNous: An LLM-Based Multi-Agent System for Grounding IT Project Management in Unstructured Conversationcited by 0
  5. 2025Towards Human-Centered RegTech: Unpacking Professionals' Strategies and Needs for Using LLMs Safelycited by 0