Effort and Size Estimation in Software Projects with Large Language Model-based Intelligent Interfaces
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Abstract (arXiv)
The advancement of Large Language Models (LLM) has also resulted in an equivalent proliferation in its applications. Software design, being one, has gained tremendous benefits in using LLMs as an interface component that extends fixed user stories. However, inclusion of LLM-based AI agents in software design often poses unexpected challenges, especially in the estimation of development efforts. Through the example of UI-based user stories, we provide a comparison against traditional methods and propose a new way to enhance specifications of natural language-based questions that allows for the estimation of development effort by taking into account data sources, interfaces and algorithms.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 4 of its 14 references that are in the map. First posted on arXiv 11 Feb 2024.
Builds on
4 of 14 references.
References · 4
- 2023GPT-4 Technical Reportcited by 18,396
- 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
- 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
- 2024Mixtral of Expertscited by 1,823
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.
- 2026Five Fatal Assumptions: Why T-Shirt Sizing Systematically Fails for AI Projects18 Feb 2026
- 2025Do LLMs Dream of Discrete Algorithms?29 Jun 2025