scholarly journals Towards a Data-Driven Requirements Elicitation Tool through the Lens of Design Thinking

2021 ◽  
Author(s):  
José de Souza Filho ◽  
Walter Nakamura ◽  
Lígia Teixeira ◽  
Rógenis da Silva ◽  
Bruno Gadelha ◽  
...  
Keyword(s):  
Design Thinking ◽  
Requirements Elicitation ◽  
Data Driven

Organizing the Design Thinking Toolbox: Supporting the Requirements Elicitation Decision Making

2021 ◽  
Author(s):  
Maria Meireles ◽  
Anderson Souza ◽  
Tayana Conte ◽  
José Maldonado
Keyword(s):  
Decision Making ◽  
Design Thinking ◽  
Requirements Elicitation

scholarly journals Design Thinking: Challenges for Software Requirements Elicitation

Information ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 371 ◽  
Author(s):  
Hugo Ferreira Martins ◽  
Antônio Carvalho de Oliveira Junior ◽  
Edna Dias Canedo ◽  
Ricardo Ajax Dias Kosloski ◽  
Roberto Ávila Paldês ◽  
...  
Keyword(s):  
Design Thinking ◽  
Requirements Elicitation ◽  
Software Requirements ◽  
Agile Methods ◽  
Software Project ◽  
Functional Requirements ◽  
Use Of Data ◽  
Development Teams ◽  
Software Development Teams ◽  
User Workshop

Agile methods fit well for software development teams in the requirements elicitation activities. It has brought challenges to organizations in adopting the existing traditional methods, as well as new ones. Design Thinking has been used as a requirements elicitation technique and immersion in the process areas, which brings the client closer to the software project team and enables the creation of better projects. With the use of data triangulation, this paper brings a literature review that collected the challenges in software requirements elicitation in agile methodologies and the use of Design Thinking. The result gave way to a case study in a Brazilian public organization project, via user workshop questionnaire with 20 items, applied during the study, in order to identify the practice of Design Thinking in this context. We propose here an overview of 13 studied challenges, from which eight presented strong evidence of contribution (stakeholders involvement, requirements definition and validation, schedule, planning, requirement details and prioritization, and interdependence), three presented partial evidence of contribution and two were not eligible for conclusions (non-functional requirements, use of artifacts, and change of requirements). The main output of this work is to present an analysis of the use of Design Thinking to see if it fits properly to be used as a means of solving the challenges of elicitation of software requirements when using agile methods.

Design Thinking and Scrum in Software Requirements Elicitation: A Case Study

2019 ◽  
pp. 179-194
Author(s):  
Rafael dos Santos Braz ◽  
José Reinaldo Merlin ◽  
Daniela Freitas Guilhermino Trindade ◽  
Carlos Eduardo Ribeiro ◽  
Ederson Marcos Sgarbi ◽  
...  
Keyword(s):  
Design Thinking ◽  
Requirements Elicitation ◽  
Software Requirements

scholarly journals An Experimental Study of the Use of Design Thinking as a Requirements Elicitation Approach for Mobile Learning Environments

2015 ◽  
Author(s):  
Cynara Lira De Carvalho Souza ◽  
Carla Silva
Keyword(s):  
Learning Environments ◽  
Mobile Devices ◽  
Mobile Learning ◽  
Teaching And Learning ◽  
Design Thinking ◽  
Research Field ◽  
Requirements Elicitation ◽  
Effective Implementation ◽  
Innovative Solutions ◽  
Thinking Process

Mobile learning (m-learning) is a research field that aims to analyze how mobile devices can contribute to learning. The development of software for mobile devices to support learning is essential for an effective implementation of m-learning or mobile learning environments (MLE). Requirements Engineering processes need to include activities that provoke creativity in the stakeholders to conceive MLEs that actually modify and improve the teaching and learning process. In this context, this paper presents a process for requirements elicitation and documentation of mobile learning environments. This process is based on the concepts of the Design Thinking process that provides a methodology to elicit customer needs, producing simple prototypes that eventually converge to innovative solutions. An experiment was conducted to evaluate if the proposed process contributes to create MLEs that present distinctive and interesting characteristics when compared to existing solutions for a specific problem.

Detecting IoT Applications Opportunities and Requirements Elicitation: A Design Thinking Based Approach

2020 ◽  
pp. 85-100
Author(s):  
Douglas Lima Dantas ◽  
Lucia Vilela Leite Filgueiras ◽  
Anarosa Alves Franco Brandão ◽  
Maria Cristina Machado Domingues ◽  
Maria Rosilene Ferreira
Keyword(s):  
Design Thinking ◽  
Requirements Elicitation ◽  
Iot Applications

scholarly journals Measuring Systems Engineering and Design Thinking Attitudes

2019 ◽  
Vol 1 (1) ◽  
pp. 3939-3948 ◽  
Author(s):  
Melissa T. Greene ◽  
Richard Gonzalez ◽  
Panos Y. Papalambros
Keyword(s):  
Systems Engineering ◽  
Design Thinking ◽  
Data Driven ◽  
Design Decisions ◽  
Human Centered Design ◽  
Measuring Systems ◽  
The Core ◽  
Confirmatory Factor ◽  
Scale Measure ◽  
Development And Validation

AbstractSystems engineering and design thinking have been widely seen as distinctly different processes, systems engineering being more data-driven and analytical, and design thinking being more human- centred and creative. We use the term ‘design thinking’ to encompass the plurality of human-centered design processes that seek to unpack the core values behind design decisions. With the increased awareness that both systems engineering and design thinking need each other, the effects of a possibly persisting distinction on engineers’ attitudes toward these two processes are not well understood. In this paper, we describe the development and validation of a scale for measuring individual attitudes about systems engineering and design thinking. Thematic analysis of engineering and design literature is used to derive a Likert scale reflecting these attitudes. We use exploratory and confirmatory factor analysis to test and confirm this two-factor thematic representation, resulting in a 9-item Systems Engineering and Design Thinking Scale measure of attitudes.

scholarly journals Data-Driven Requirements Elicitation: A Systematic Literature Review

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Sachiko Lim ◽  
Aron Henriksson ◽  
Jelena Zdravkovic
Keyword(s):  
Big Data ◽  
Requirements Engineering ◽  
Domain Knowledge ◽  
Final Analysis ◽  
Online Reviews ◽  
Requirements Elicitation ◽  
Supervised Machine Learning ◽  
Data Driven ◽  
Related Information ◽  
Current State

AbstractRequirements engineering has traditionally been stakeholder-driven. In addition to domain knowledge, widespread digitalization has led to the generation of vast amounts of data (Big Data) from heterogeneous digital sources such as the Internet of Things (IoT), mobile devices, and social networks. The digital transformation has spawned new opportunities to consider such data as potentially valuable sources of requirements, although they are not intentionally created for requirements elicitation. A challenge to data-driven requirements engineering concerns the lack of methods to facilitate seamless and autonomous requirements elicitation from such dynamic and unintended digital sources. There are numerous challenges in processing the data effectively to be fully exploited in organizations. This article, thus, reviews the current state-of-the-art approaches to data-driven requirements elicitation from dynamic data sources and identifies research gaps. We obtained 1848 hits when searching six electronic databases. Through a two-level screening and a complementary forward and backward reference search, 68 papers were selected for final analysis. The results reveal that the existing automated requirements elicitation primarily focuses on utilizing human-sourced data, especially online reviews, as requirements sources, and supervised machine learning for data processing. The outcomes of automated requirements elicitation often result in mere identification and classification of requirements-related information or identification of features, without eliciting requirements in a ready-to-use form. This article highlights the need for developing methods to leverage process-mediated and machine-generated data for requirements elicitation and addressing the issues related to variety, velocity, and volume of Big Data for the efficient and effective software development and evolution.

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