Unifying and Extending User Story Models

Wautelet, Yves; Heng, Samedi; Kolp, Manuel; Mirbel, Isabelle

doi:10.1007/978-3-319-07881-6_15

Cited by 96 publications

(52 citation statements)

References 8 publications

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“…Based on previous work [15,37,66], we define a generic representation of user stories-see the UML class diagram in Fig. 1-that dissects a user story into its constituents.…”

Section: Conceptual Anatomy Of User Storiesmentioning

confidence: 99%

“…1-that dissects a user story into its constituents. User stories follow a standard predefined format [66] to capture three aspects of a requirement:…”

Section: Conceptual Anatomy Of User Storiesmentioning

confidence: 99%

“…Our tool only accepts user stories that use the indicators as identified by Wautelet et al [66]: As / As a(n) for the role, I want (to) / I can / I am able / I would like for the means, and so that for the ends part. Syntactically invalid user stories are not processed; in order to sanitize these stories, analysts should preprocess them using tools such as AQUSA as shown in Fig.…”

Section: The Visual Narrator Toolmentioning

confidence: 99%

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Extracting conceptual models from user stories with Visual Narrator

et al. 2017

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Extracting conceptual models from natural language requirements can help identify dependencies, redundancies, and conflicts between requirements via a holistic and easy-to-understand view that is generated from lengthy textual specifications. Unfortunately, existing approaches never gained traction in practice, because they either require substantial human involvement or they deliver too low accuracy. In this paper, we propose an automated approach called Visual Narrator based on natural language processing that extracts conceptual models from user story requirements. We choose this notation because of its popularity among (agile) practitioners and its focus on the essential components of a requirement: Who? What? Why? Coupled with a careful selection and tuning of heuristics, we show how Visual Narrator enables generating conceptual models from user stories with high accuracy. Visual Narrator is part of the holistic Grimm method for user story collaboration that ranges from elicitation to the interactive visualization and analysis of requirements.

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“…Based on previous work [15,37,66], we define a generic representation of user stories-see the UML class diagram in Fig. 1-that dissects a user story into its constituents.…”

Section: Conceptual Anatomy Of User Storiesmentioning

confidence: 99%

“…1-that dissects a user story into its constituents. User stories follow a standard predefined format [66] to capture three aspects of a requirement:…”

Section: Conceptual Anatomy Of User Storiesmentioning

confidence: 99%

Section: The Visual Narrator Toolmentioning

confidence: 99%

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Extracting conceptual models from user stories with Visual Narrator

et al. 2017

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“…Although originally proposed as unstructured text similar to use cases [2] but restricted in size [10], nowadays user stories follow a strict, compact template that captures who it is for, what it expects from the system, and (optionally) why it is important [54].…”

Section: A Conceptual Model Of User Storiesmentioning

confidence: 99%

“…A user story should follow some pre-defined, agreed upon template chosen from the many existing ones [54]. The skeleton of the template is called format in the conceptual model, in between which the role, means, and optional end(s) are interspersed to form a user story.…”

Section: Formatmentioning

confidence: 99%

Improving agile requirements: the Quality User Story framework and tool

et al. 2016

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User stories are a widely adopted requirements notation in agile development. Yet, user stories are too often poorly written in practice and exhibit inherent quality defects. Triggered by this observation, we propose the Quality User Story (QUS) framework, a set of 13 quality criteria that user story writers should strive to conform to. Based on QUS, we present the Automatic Quality User Story Artisan (AQUSA) software tool. Relying on natural language processing (NLP) techniques, AQUSA detects quality defects and suggest possible remedies. We describe the architecture of AQUSA, its implementation, and we report on an evaluation that analyzes 1023 user stories obtained from 18 software companies. Our tool does not yet reach the ambitious 100 % recall that Daniel Berry and colleagues require NLP tools for RE to achieve. However, we obtain promising results and we identify some improvements that will substantially improve recall and precision.

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BIM‐basierte Planfeststellung für Infrastrukturgroßprojekte

Matthei,

Rausch,

Klemt‐Albert

2024

Bautechnik

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Die Planfeststellung markiert einen entscheidenden Meilenstein im Realisierungsprozess von Infrastrukturgroßprojekten. Diese Phase ist jedoch nicht selten von zeitaufwendigen Verfahren und damit einhergehenden steigenden Kosten geprägt. Angesichts der dringenden Notwendigkeit, effiziente und nachhaltige Infrastrukturlösungen zu entwickeln, gewinnt die Digitalisierung und Beschleunigung von Genehmigungsverfahren wie dem Planfeststellungsverfahren (PFV) zunehmend an Bedeutung. In diesem Kontext rückt die Methode des Building Information Modeling (BIM) als vielversprechende Lösung in den Fokus. Basierend auf einer empirischen Untersuchung identifiziert dieser Aufsatz 13 Optimierungsmöglichkeiten im PFV, die in Form von User Storys festgehalten werden. Auf Grundlage dieser Erkenntnisse wird ein Konzept zur BIM‐basierten Planfeststellung entwickelt, welches einen konkreten Ansatz darstellt, wie Genehmigungsverfahren beschleunigt und bürgernäher gestaltet werden können. Das entworfene Konzept befähigt den Vorhabenträger sowie die Planfeststellungsbehörde, den Prozess der Planfeststellung durch Nutzung einer gemeinsamen Datenplattform (Common Data Environment, CDE) zu koordinieren und sämtliche relevanten Daten und Informationen auszutauschen. Das entwickelte Konzept gilt es im Rahmen des Forschungsprojekts BIM4People (08/23–01/26, 19FS2057A) zu konkretisieren und um Anforderungen zu ergänzen.

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Unifying and Extending User Story Models

Cited by 96 publications

References 8 publications

Extracting conceptual models from user stories with Visual Narrator

Extracting conceptual models from user stories with Visual Narrator

Improving agile requirements: the Quality User Story framework and tool

BIM‐basierte Planfeststellung für Infrastrukturgroßprojekte

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