Improving agile requirements: the Quality User Story framework and tool. Lucassen, G., Dalpiaz, F., van der Werf, J. M. E. M., & Brinkkemper, S. Requirements Engineering, 21(3):383–403, September, 2016. titleTranslation: تحسين المتطلبات الرشيقة: إطار قصة مستخدم الجودة والأداة
Paper doi abstract bibtex 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.
@article{lucassen_improving_2016,
title = {Improving agile requirements: the {Quality} {User} {Story} framework and tool},
volume = {21},
issn = {1432-010X},
shorttitle = {Improving agile requirements},
url = {https://doi.org/10.1007/s00766-016-0250-x},
doi = {10.1007/s00766-016-0250-x},
abstract = {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.},
language = {en},
number = {3},
urldate = {2023-06-08},
journal = {Requirements Engineering},
author = {Lucassen, Garm and Dalpiaz, Fabiano and van der Werf, Jan Martijn E. M. and Brinkkemper, Sjaak},
month = sep,
year = {2016},
note = {titleTranslation: تحسين المتطلبات الرشيقة: إطار قصة مستخدم الجودة والأداة},
keywords = {AQUSA, Multi-case study, Natural language processing, QUS framework, Requirements quality, User stories},
pages = {383--403},
}
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