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User story

About: User story is a research topic. Over the lifetime, 1078 publications have been published within this topic receiving 23717 citations.


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Book ChapterDOI
01 Jan 2016
TL;DR: A state of the art of the elicitation models that makes simultaneous use of two well-known techniques: the use cases model and user stories is provided.
Abstract: Requirement elicitation (RE) is one of the main tasks that must be performed in order to guarantee the correct implementation of a software development. Its incorrect specification can cause unnecessary overdue costs for the project and, in some cases, its complete failure. The objective of this paper is to provide a state of the art of the elicitation models that makes simultaneous use of two well-known techniques: the use cases model and user stories. The systematic literature review was chosen as a supportive investigation methodology. From the 45 found publications, the search strategy identified 11 studies and 3 methodological proposals: Athena, K-gileRE and NORMAP. Finally, after having reviewed the literature, it was found that there are a few validated proposals that makes use of the combination of user stories and use cases models. Also, there is not enough information to acknowledge the actual efficacy of combining both techniques.

3 citations

Book ChapterDOI
01 Jan 2021
TL;DR: This chapter discusses the difficulty in making the data useful for the general public, and elaborate on a self-organizing agile approach to developing an urban big data infrastructure.
Abstract: Urban big data often contain spatial and temporal elements that have increasingly become an integral part of various applications and projects such as smart mobility, smart city, and other digitally enhanced urban infrastructure. It is critical to develop an open and collaborative environment so that these data can be used by a wide range of users. This chapter first discusses some characteristics and sources of urban big data. Three hypothetical user stories are described to highlight the potential of these data. After describing the internal data structure of these data and techniques that can be used to retrieve the data, we discuss the difficulty in making the data useful for the general public and elaborate on a self-organizing agile approach to developing an urban big data infrastructure.

3 citations

Proceedings ArticleDOI
22 Jul 2019
TL;DR: With the application of augmented reality and the uncertainty caused by random probability, the effect of gamification was found to increase the desire to learn, and strengthened cognitive and observational powers which were helpful in describing the user's story.
Abstract: The purpose of this study is to train students in the university's design department to organize and demonstrate the story of a user's situational intentions in the design process. The user context investigation is an integral step in any design activity, and it helps to better understand the target of the design, meet the users' needs, and provide the correct design direction in order to reduce the failure rate after a product goes on the market. Students in the design department have often been found to lack complete product design thinking, and the students' design is often out of touch with reality and cannot meet the needs of end users. The aim of this research is to investigate how to combine physical objects and augmented reality by randomly generating three-dimensional objects combining people, objects and scenes, and having students tell the user stories based on the results produced. Through logic and insight, the future is then applied to the observation of actual scenes. In this study, augmented reality and 3D printing techniques were used to create three teaching aids, each presented in a hexahedral physical form. Each teaching aid contained six randomly generated 3D objects. The random control was thus the students who had a total of 216 permutations. The results of the application are more integrated and more effective in the classroom, and students were found to be highly interested in interacting with the system. With the application of augmented reality and the uncertainty caused by random probability, the effect of gamification was found to increase the desire to learn, and strengthened cognitive and observational powers which were helpful in describing the user's story.

3 citations

Proceedings ArticleDOI
01 Dec 2017
TL;DR: In this paper, the authors propose a strategy to classify the US following the taxonomy of Bloom to determine the degree of its complexity and by doing so make an agile estimation of the approximate time that each one requires for its realization.
Abstract: Agile methodologies are increasingly adopted by companies, these follow software engineering methods based on iterative and incremental development. Among the most popular is the framework of SCRUM (1986), which is characterized by iterations, where in each iteration it is necessary to make a planning, analysis of requirements, design, coding, tests and documentation. The requirements specification in SCRUM is based on the concept of user stories (US), which are the description of a requirement written in one or two sentences using the user's natural language. An important feature of US is that they must be estimable, that is to say, it must be possible to determine the time it will take to complete it. This allows the Scrum Development Team to determine the total time of the project. However in practice, this task still has problems due to the complexity of the requirements and affects the agreement of US in each iteration. In this paper we propose a strategy to classify the US following the taxonomy of Bloom to determine the degree of its complexity and by doing so make an agile estimation of the approximate time that each one requires for its realization. We show the results obtained after the US have been classified from two projects and based on them, we propose strategies to estimate them.

3 citations

Journal ArticleDOI
TL;DR: The proposed machine learning approach is evaluated on a set of user stories from real-world mobile enterprise application development projects and suggests that machine learning approaches can be beneficially applied to user story classification in large companies.
Abstract: Mobile enterprise applications (apps) are developed in dynamic and complex environments. Hardware characteristics, operating systems and development tools are constantly changing. In larger institutions, comprehensive corporate guidelines and requirements have to be followed. In addition, larger enterprises often develop numerous apps and lack an overview of development projects. Because of the size of such companies, a comprehensive direct information exchange between developers is often not practicable. In this situation, IT support is necessary, for example to prevent unnecessary duplication of work in the development of software artifacts such as user stories, app screen designs or code features within the company. One approach to overcome these challenges is to support reusing results from previous projects by building systems to organize and analyse the knowledge base of enterprise app development projects. For such systems an automatic categorization of artifacts is required. In this work we propose using a machine learning approach to categorize user stories. The approach is evaluated on a set of user stories from real-world mobile enterprise application development projects. The results are promising and suggest that machine learning approaches can be beneficially applied to user story classification in large companies.

3 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202334
202259
202157
202084
201991
201875