StartFlow: From Method Conception to Multi-Perspective Evaluation in UX Prototyping for Software Startups

• URL: http://arxiv.org/abs/2605.10824v1
• Date: Mon, 11 May 2026 16:43:21 GMT
• Title: StartFlow: From Method Conception to Multi-Perspective Evaluation in UX Prototyping for Software Startups
• Abstract summary: StartFlow is a structured method that helps non-specialized professionals create MVP.<n>This study focuses on the use of StartFlow in early stages of software development.
• Score: 1.0499611180329804
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Context. Software startups face significant challenges in building minimum viable products, particularly in the early stages, when resources are limited and expertise in user experience is scarce. Objective. Introduce StartFlow, a structured method that helps non-specialized professionals create MVP prototypes using the wireflow technique, a combination of wireframes and user flows. StartFlow consists of three steps: (i) organizing features; (ii) building wireflows; and (iii) verifying and refining them based on usability heuristics. Method. To assess the method Startflow, we first conducted a focus group with researchers in Software Engineering, Human-Computer Interaction, and Software Startups. Afterward, we conducted a proof-of-concept study, which consisted of an experiment and a heuristic evaluation with experts. Results. The qualitative analysis of the focus group revealed that participants found the method straightforward, flexible, and helpful in structuring user flows and identifying visual components. However, they also pointed out the need to improve its presentation, clarify its iterative nature, and strengthen its connection to broader UX principles. The results of the proof-of-concept indicate that participants who used StartFlow created clearer prototypes, adhered to the proposed user stories and business rules, and presented fewer usability defects. Furthermore, the method was well evaluated for its ease of use and intended future adoption. Conclusion. The study reinforces the potential of StartFlow as an accessible tool to support user-centered development in software startups from the earliest stages of their product development.

Related papers

• Knowledge-Centric Agents for Workflow Generation [73.4340391112949]
  We propose a knowledge-centric framework that learns to invert, inject, and infer with knowledge across multiple abstraction levels.<n>Our method produces with richer node diversity, more coherent structures, and higher execution success rates than existing systems.
  arXiv  Detail & Related papers  (2026-07-17T11:03:01Z)
• SoftBoard: A Multi-Agent Tool for the Creation and Evaluation of Low-Fidelity Prototypes [0.0]
  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.
  arXiv  Detail & Related papers  (2026-07-14T18:27:55Z)
• Towards Agentic Intelligence for Materials Science [73.4576385477731]
  This survey advances a unique pipeline-centric view that spans from corpus curation and pretraining to goal-conditioned agents interfacing with simulation and experimental platforms.<n>To bridge communities and establish a shared frame of reference, we first present an integrated lens that aligns terminology, evaluation, and workflow stages across AI and materials science.
  arXiv  Detail & Related papers  (2026-01-29T23:48:43Z)
• QoNext: Towards Next-generation QoE for Foundation Models [63.76972456980632]
  Existing evaluations of foundation models fail to capture what truly matters: user's experience during interaction.<n>We introduce QoNext, the first framework that adapts Quality of Experience principles to the assessment of foundation models.<n>We construct a QoE-oriented database and train predictive models that estimate perceived user experience from measurable system parameters.
  arXiv  Detail & Related papers  (2025-09-26T05:26:52Z)
• Data Analysis in the Era of Generative AI [56.44807642944589]
  This paper explores the potential of AI-powered tools to reshape data analysis, focusing on design considerations and challenges. We explore how the emergence of large language and multimodal models offers new opportunities to enhance various stages of data analysis workflow. We then examine human-centered design principles that facilitate intuitive interactions, build user trust, and streamline the AI-assisted analysis workflow across multiple apps.
  arXiv  Detail & Related papers  (2024-09-27T06:31:03Z)
• An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models [55.01592097059969]
  Supervised finetuning on instruction datasets has played a crucial role in achieving the remarkable zero-shot generalization capabilities. Active learning is effective in identifying useful subsets of samples to annotate from an unlabeled pool. We propose using experimental design to circumvent the computational bottlenecks of active learning.
  arXiv  Detail & Related papers  (2024-01-12T16:56:54Z)
• Need-driven decision-making and prototyping for DLT: Framework and web-based tool [0.0]
  Multiple groups attempted to disentangle the technology from the associated hype and controversy. We develop a holistic analytical framework and open-source web tool for making evidence-based decisions.
  arXiv  Detail & Related papers  (2023-07-18T12:19:47Z)
• Generative User-Experience Research for Developing Domain-specific Natural Language Processing Applications [4.139846693958609]
  This paper proposes a new methodology for integrating generative UX research into developing domain NLP applications. Generative UX research employs domain users at the initial stages of prototype development, i.e., ideation and concept evaluation, and the last stage for evaluating system usefulness and user utility.
  arXiv  Detail & Related papers  (2023-06-28T12:17:45Z)
• Nemo: Guiding and Contextualizing Weak Supervision for Interactive Data Programming [77.38174112525168]
  We present Nemo, an end-to-end interactive Supervision system that improves overall productivity of WS learning pipeline by an average 20% (and up to 47% in one task) compared to the prevailing WS supervision approach.
  arXiv  Detail & Related papers  (2022-03-02T19:57:32Z)
• HyMap: eliciting hypotheses in early-stage software startups using cognitive mapping [10.60958748634425]
  We aim to develop a technique to identify hypotheses for early-stage software startups. We developed the HyMap, a hypotheses elicitation technique based on cognitive mapping.
  arXiv  Detail & Related papers  (2021-02-18T17:29:47Z)
• Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling [19.24454872492008]
  Weak supervision offers a promising alternative for producing labeled datasets without ground truth labels. We develop the first framework for interactive weak supervision in which a method proposes iterations and learns from user feedback. Our experiments demonstrate that only a small number of feedback are needed to train models that achieve highly competitive test set performance.
  arXiv  Detail & Related papers  (2020-12-11T00:10:38Z)

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.