%0 Conference Proceedings
%T Evaluating the Impact of User Stories Quality on the Ability to Understand and Structure Requirements
%+ Catholic University of Leuven = Katholieke Universiteit Leuven (KU Leuven)
%+ HEC Liège
%A Wautelet, Yves
%A Gielis, Dries
%A Poelmans, Stephan
%A Heng, Samedi
%Z Part 1: Requirements
%< avec comité de lecture
%( Lecture Notes in Business Information Processing
%B 12th IFIP Working Conference on The Practice of Enterprise Modeling (PoEM)
%C Luxembourg, Luxembourg
%Y Jaap Gordijn
%Y Wided Guédria
%Y Henderik A. Proper
%I Springer International Publishing
%3 The Practice of Enterprise Modeling
%V LNBIP-369
%P 3-19
%8 2019-11-27
%D 2019
%R 10.1007/978-3-030-35151-9_1
%K User Stories
%K Rationale Tree
%K Quality User Story
%K Modeling experiment
%Z Computer Science [cs]
%Z Humanities and Social Sciences/Library and information sciencesConference papers
%X Scrum is driven by user stories (US). The development team indeed uses, to fill the project’s and the sprints’ backlog, sentences describing the user expectations with respect to the software. US are often written “on the fly” in structured natural language so their quality and the set’s consistency are not ensured. The Quality User Story (QUS) framework intends to evaluate and improve the quality of a given US set. Other independent research has built a unified model for tagging the elements of the WHO, WHAT and WHY dimensions of a US; each tag representing a concept with an inherent nature and granularity. Once tagged, the US elements can be graphically represented through an icon and the modeler can link them when inter-dependencies are identified to build one or more Rationale Trees (RT). This paper presents the result of an experiment conducted with novice modelers aimed to evaluate how well they are able to build a RT out of (i) a raw real-life US set (group 1) and (ii) a new version of the US set improved in quality using QUS (group 2).The experiment requires test subjects to identify the nature of US elements and to graphically represent and link them. The QUS-compliant US set improved the ability of the test subjects to make this identification and linking. We cannot conclude that the use of the QUS framework improved the understanding of the problem/solution domain but when a QUS-compliant US set is used to build a RT, it increases the ability of modelers to identify Epic US. Building a RT thus has a positive impact on identifying the structure of a US set’s functional elements.
%G English
%Z TC 8
%Z WG 8.1
%2 https://inria.hal.science/hal-03231362/document
%2 https://inria.hal.science/hal-03231362/file/491976_1_En_1_Chapter.pdf
%L hal-03231362
%U https://inria.hal.science/hal-03231362
%~ SHS
%~ IFIP
%~ IFIP-TC
%~ IFIP-LNBIP
%~ IFIP-WG
%~ IFIP-TC8
%~ IFIP-WG8-1
%~ IFIP-POEM
%~ IFIP-LNBIP-369