# ARLO: A Tailorable Approach for Transforming Natural Language Software Requirements into Architecture using LLMs

arXiv: https://arxiv.org/abs/2504.06143 · HTML: https://mlsanity.com/paper/2504.06143/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 71 references that are in the map.
- First posted on arXiv 8 Apr 2025.

## Builds on (most cited first, 8 of 8)

- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment](https://mlsanity.com/paper/2303.16634/) (2023)
- [ChatGPT Prompt Patterns for Improving Code Quality, Refactoring, Requirements Elicitation, and Software Design](https://mlsanity.com/paper/2303.07839/) (2023)
- [Hierarchical Clustering: Objective Functions and Algorithms](https://mlsanity.com/paper/1704.02147/) (2017)
- [Lessons from the Use of Natural Language Inference (NLI) in Requirements Engineering Tasks](https://mlsanity.com/paper/2405.05135/) (2024)
- [A deep learning model for estimating story points](https://mlsanity.com/paper/1609.00489/) (2016)
- [From Requirements to Architecture: An AI-Based Journey to Semi-Automatically Generate Software Architectures](https://mlsanity.com/paper/2401.14079/) (2024)
- [Exploring Web Search Engines to Find Architectural Knowledge](https://mlsanity.com/paper/2103.11705/) (2021)

## Built on by (most cited first, 1 of 1)

- [Automating Domain-Driven Design: Experience with a Prompting Framework](https://mlsanity.com/paper/2603.26244/) (2026)

## Abstract (arXiv)

Software requirements expressed in natural language (NL) frequently suffer from verbosity, ambiguity, and inconsistency. This creates a range of challenges, including selecting an appropriate architecture for a system and assessing different architectural alternatives. Relying on human expertise to accomplish the task of mapping NL requirements to architecture is time-consuming and error-prone. This paper proposes ARLO, an approach that automates this task by leveraging (1) a set of NL requirements for a system, (2) an existing standard that specifies architecturally relevant software quality attributes, and (3) a readily available Large Language Model (LLM). Specifically, ARLO determines the subset of NL requirements for a given system that is architecturally relevant and maps that subset to a tailorable matrix of architectural choices. ARLO applies integer linear programming on the architectural-choice matrix to determine the optimal architecture for the current requirements. We demonstrate ARLO's efficacy using a set of real-world examples. We highlight ARLO's ability (1) to trace the selected architectural choices to the requirements and (2) to isolate NL requirements that exert a particular influence on a system's architecture. This allows the identification, comparative assessment, and exploration of alternative architectural choices based on the requirements and constraints expressed therein.