Systematic Mapping Protocol -- UX Design role in software development process

• URL: http://arxiv.org/abs/2402.13143v1
• Date: Tue, 20 Feb 2024 16:56:46 GMT
• Title: Systematic Mapping Protocol -- UX Design role in software development process
• Authors: Emilio Orme\~no, Fernando Pinciroli
• Abstract summary: We present a systematic mapping protocol for investigating the role of the UX designer in the software development process. We define the research questions, scope, sources, search strategy, selection criteria, data extraction, and analysis methods that we will use to conduct the mapping study.
• Score: 55.2480439325792
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A systematic mapping protocol is a method for conducting a literature review in a rigorous and transparent way. It aims to provide an overview of the current state of research on a specific topic, identify gaps and opportunities, and guide future work. In this document, we present a systematic mapping protocol for investigating the role of the UX designer in the software development process. We define the research questions, scope, sources, search strategy, selection criteria, data extraction, and analysis methods that we will use to conduct the mapping study. Our goal is to understand how the UX designers collaborate with other stakeholders, what methods and tools they use, what challenges they face, and what outcomes they achieve in different contexts and domains.

Related papers

• Customized Information and Domain-centric Knowledge Graph Construction with Large Language Models [0.0]
  We propose a novel approach based on knowledge graphs to provide timely access to structured information. Our framework encompasses a text mining process, which includes information retrieval, keyphrase extraction, semantic network creation, and topic map visualization. We apply our methodology to the domain of automotive electrical systems to demonstrate the approach, which is scalable.
  arXiv  Detail & Related papers  (2024-09-30T07:08:28Z)
• SyROCCo: Enhancing Systematic Reviews using Machine Learning [6.805429133535976]
  This paper explores the use of machine learning techniques to help navigate the systematic review process. The application of ML techniques to subsequent stages of a review, such as data extraction and evidence mapping, is in its infancy.
  arXiv  Detail & Related papers  (2024-06-24T11:04:43Z)
• Ontology Embedding: A Survey of Methods, Applications and Resources [54.3453925775069]
  Ontologies are widely used for representing domain knowledge and meta data. One straightforward solution is to integrate statistical analysis and machine learning. Numerous papers have been published on embedding, but a lack of systematic reviews hinders researchers from gaining a comprehensive understanding of this field.
  arXiv  Detail & Related papers  (2024-06-16T14:49:19Z)
• Enhancing Text Corpus Exploration with Post Hoc Explanations and Comparative Design [6.8863648800930655]
  Text corpus exploration (TCE) spans the range of exploratory search tasks. Current systems lack the flexibility to support the range of tasks encountered in practice. We provide methods that enhance TCE tools with post hoc explanations and multiscale, comparative designs.
  arXiv  Detail & Related papers  (2024-06-14T03:13:58Z)
• Embedding in Recommender Systems: A Survey [67.67966158305603]
  A crucial aspect is embedding techniques that covert the high-dimensional discrete features, such as user and item IDs, into low-dimensional continuous vectors. Applying embedding techniques captures complex entity relationships and has spurred substantial research. This survey covers embedding methods like collaborative filtering, self-supervised learning, and graph-based techniques.
  arXiv  Detail & Related papers  (2023-10-28T06:31:06Z)
• SoK: Privacy-Preserving Data Synthesis [72.92263073534899]
  This paper focuses on privacy-preserving data synthesis (PPDS) by providing a comprehensive overview, analysis, and discussion of the field. We put forth a master recipe that unifies two prominent strands of research in PPDS: statistical methods and deep learning (DL)-based methods.
  arXiv  Detail & Related papers  (2023-07-05T08:29:31Z)
• AVIS: Autonomous Visual Information Seeking with Large Language Model Agent [123.75169211547149]
  We propose an autonomous information seeking visual question answering framework, AVIS. Our method leverages a Large Language Model (LLM) to dynamically strategize the utilization of external tools. AVIS achieves state-of-the-art results on knowledge-intensive visual question answering benchmarks such as Infoseek and OK-VQA.
  arXiv  Detail & Related papers  (2023-06-13T20:50:22Z)
• How to choose an Explainability Method? Towards a Methodical Implementation of XAI in Practice [3.974102831754831]
  We argue there is a need for a methodology to bridge the gap between stakeholder needs and explanation methods. We present our ongoing work on creating this methodology to help data scientists in the process of providing explainability to stakeholders.
  arXiv  Detail & Related papers  (2021-07-09T13:22:58Z)
• Deep Learning Schema-based Event Extraction: Literature Review and Current Trends [60.29289298349322]
  Event extraction technology based on deep learning has become a research hotspot. This paper fills the gap by reviewing the state-of-the-art approaches, focusing on deep learning-based models.
  arXiv  Detail & Related papers  (2021-07-05T16:32:45Z)
• Visual analytics of set data for knowledge discovery and member selection support [0.7734726150561089]
  We develop a method for the VA of set data aimed at supporting knowledge discovery and member selection. A typical target application is a visual support system for team analysis and member selection. We demonstrate the proposed method by applying it to basketball teams, and compare with a benchmark system for outcome prediction and lineup reconstruction tasks.
  arXiv  Detail & Related papers  (2021-04-04T08:22:01Z)

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.