SoftBoard: A Multi-Agent Tool for the Creation and Evaluation of Low-Fidelity Prototypes

• URL: http://arxiv.org/abs/2607.13179v1
• Date: Tue, 14 Jul 2026 18:27:55 GMT
• Title: SoftBoard: A Multi-Agent Tool for the Creation and Evaluation of Low-Fidelity Prototypes
• Abstract summary: Building Minimum Viable Products (MVPs) through low-fidelity prototyping represents a well-established strategy for rapid validation cycles at reduced cost.<n>This paper presents SoftBoard, a web-based tool for the creation and evaluation of low-fidelity prototypes.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: User Experience (UX) is recognized as a critical factor for the success of digital products, particularly in software startups, environments marked by time constraints, limited resources, and low maturity in design practices. Building Minimum Viable Products (MVPs) through low-fidelity prototyping represents a well-established strategy for rapid validation cycles at reduced cost. A systematic literature mapping, however, revealed gaps in the ecosystem of available tools: a predominance of general-purpose solutions adapted for prototyping, the absence of integrated methodological guidance, and the incipient use of Artificial Intelligence in the design process. This paper presents SoftBoard, a web-based tool for the creation and evaluation of low-fidelity prototypes in the context of MVP development. The tool integrates a prototype editor, team-based project organization, and a multi-agent system based on large language models that supports requirements elicitation and refinement, automates prototype generation, and evaluates interface quality based on usability heuristics. This integration aims to reduce reliance on prior UX expertise, standardize the prototyping process, and support teams in building MVPs aligned with user needs. As future work, a feasibility study with software professionals is currently underway.

Related papers

• ADEPT: A Unified Framework for Deep Learning Test Adequacy [0.4899818550820574]
  In this paper, we present the engineering details of ADEPT, a framework that integrates representative adequacy techniques.<n>ADEPT provides a template-based metric interface with well-defined extension points for integrating new adequacy metrics.<n>ADEPT is designed for researchers and practitioners who wish to reproduce and apply adequacy metrics without spending days or weeks implementing missing tooling.
  arXiv  Detail & Related papers  (2026-08-12T15:03:28Z)
• The Evolution of Tool Use in LLM Agents: From Single-Tool Call to Multi-Tool Orchestration [58.61149924275458]
  Tool use enables large language models to access external information, invoke software systems, and act in digital environments beyond what can be solved from model parameters alone.<n>As agent systems evolve, the central problem has shifted from isolated invocation to multi-tool orchestration over long trajectories with intermediate state, execution feedback, changing environments, and practical constraints such as safety, cost, and verifiability.<n>We organize the literature around six core dimensions: inference-time planning and execution, training and trajectory construction, safety and control, efficiency under resource constraints, capability completeness in open environments, and benchmark design and evaluation.
  arXiv  Detail & Related papers  (2026-03-24T07:05:05Z)
• Deep Research for Recommender Systems [74.44288812684448]
  We propose a novel deep research paradigm for recommendation, which replaces conventional item lists with comprehensive, user-centric reports.<n>We instantiate this paradigm through RecPilot, a multi-agent framework comprising two core components: a user trajectory simulation agent that autonomously explores the item space, and a self-evolving report generation agent that synthesizes the findings into a coherent, interpretable report tailored to support user decisions.
  arXiv  Detail & Related papers  (2026-03-08T12:33:04Z)
• Proto-ML: An IDE for ML Solution Prototyping [0.764671395172401]
  Proto-ML is a machine learning (ML) prototyping tool designed to strengthen stakeholder involvement.<n>Proto-ML offers a unified framework that enables structured documentation of prototyping activities and promotes knowledge sharing.<n>Preliminary user feedback suggests that Proto-ML can increase prototyping efficiency and foster more transparent and reusable ML solution development.
  arXiv  Detail & Related papers  (2026-02-25T09:43:56Z)
• OpenSage: Self-programming Agent Generation Engine [56.399761469404496]
  We propose OpenSage, the first agent development kit (ADK) to automatically create agents with self-generated topology and toolsets.<n>OpenSage offers effective functionality for agents to create and manage their own sub-agents and toolkits.<n>We believe OpenSage can pave the way for the next generation of agent development, shifting the focus from human-centered to AI-centered paradigms.
  arXiv  Detail & Related papers  (2026-02-18T21:16:29Z)
• AI-NativeBench: An Open-Source White-Box Agentic Benchmark Suite for AI-Native Systems [52.65695508605237]
  We introduce AI-NativeBench, the first application-centric and white-box AI-Native benchmark suite grounded in Model Context Protocol (MCP) and Agent-to-Agent (A2A) standards.<n>By treating agentic spans as first-class citizens within distributed traces, our methodology enables granular analysis of engineering characteristics beyond simple capabilities.<n>This work provides the first systematic evidence to guide the transition from measuring model capability to engineering reliable AI-Native systems.
  arXiv  Detail & Related papers  (2026-01-14T11:32:07Z)
• A Serverless Architecture for Real-Time Stock Analysis using Large Language Models: An Iterative Development and Debugging Case Study [0.0]
  This paper documents the design, implementation, and iterative debug of a novel, serverless system for real-time stock analysis.<n>We detail the architectural evolution of the system, from initial concepts to a robust, event-driven pipeline.<n>The final architecture operates at a near-zero cost, demonstrating a viable model for individuals to build sophisticated AI-powered financial tools.
  arXiv  Detail & Related papers  (2025-07-13T11:29:51Z)
• Establishing tool support for a concept DSL [0.0]
  This thesis describes Conceptual, a DSL for modeling the behavior of software systems using self-contained and highly reusable units of concepts.<n>The suggested strategy is then implemented with a simple compiler, allowing developers to access and utilize Alloy's existing analysis tools for program reasoning.
  arXiv  Detail & Related papers  (2025-03-07T09:18:31Z)
• Sketch2Code: Evaluating Vision-Language Models for Interactive Web Design Prototyping [55.98643055756135]
  We introduce Sketch2Code, a benchmark that evaluates state-of-the-art Vision Language Models (VLMs) on automating the conversion of rudimentary sketches into webpage prototypes. We analyze ten commercial and open-source models, showing that Sketch2Code is challenging for existing VLMs. A user study with UI/UX experts reveals a significant preference for proactive question-asking over passive feedback reception.
  arXiv  Detail & Related papers  (2024-10-21T17:39:49Z)
• Interlinking User Stories and GUI Prototyping: A Semi-Automatic LLM-based Approach [55.762798168494726]
  We present a novel Large Language Model (LLM)-based approach for validating the implementation of functional NL-based requirements in a graphical user interface (GUI) prototype. Our approach aims to detect functional user stories that are not implemented in a GUI prototype and provides recommendations for suitable GUI components directly implementing the requirements.
  arXiv  Detail & Related papers  (2024-06-12T11:59:26Z)
• Prototyping with Prompts: Emerging Approaches and Challenges in Generative AI Design for Collaborative Software Teams [2.237039275844699]
  Generative AI models are increasingly being integrated into human task, enabling the production of expressive content.<n>Unlike traditional human-AI design methods, the new approach to designing generative capabilities focuses heavily on prompt engineering strategies.<n>Our findings highlight emerging practices and role shifts in AI system prototyping among multistakeholder teams.
  arXiv  Detail & Related papers  (2024-02-27T17:56:10Z)
• U-TOE: Universal TinyML On-board Evaluation Toolkit for Low-Power IoT [3.981958767941474]
  U-TOE is a universal toolkit designed to facilitate the task of IoT designers and researchers. We provide an open source implementation of U-TOE and demonstrate its use to experimentally evaluate the performance of various models.
  arXiv  Detail & Related papers  (2023-06-26T10:35:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.