# Effort and Size Estimation in Software Projects with Large Language Model-based Intelligent Interfaces

arXiv: https://arxiv.org/abs/2402.07158 · HTML: https://mlsanity.com/paper/2402.07158/

## 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 (most cited first, 4 of 4)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Mixtral of Experts](https://mlsanity.com/paper/2401.04088/) (2024)

## Built on by (most cited first, 2 of 2)

- [Do LLMs Dream of Discrete Algorithms?](https://mlsanity.com/paper/2506.23408/) (2025)
- [Five Fatal Assumptions: Why T-Shirt Sizing Systematically Fails for AI Projects](https://mlsanity.com/paper/2602.17734/) (2026)

## 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.