The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
L'affinement des objectifs est une étape cruciale dans l'analyse des exigences orientée vers les objectifs afin de créer un modèle d'objectifs de haute qualité. Un mauvais affinement des objectifs conduit à des exigences manquantes et à des exigences incorrectes ainsi qu'à une moins grande exhaustivité des modèles d'objectifs produits. Cet article propose une technique pour automatiser la détection mauvaises odeurs de raffinement des objectifs, symptômes d’un mauvais raffinement des objectifs. Dans un premier temps, pour clarifier les mauvaises odeurs, nous avons demandé aux sujets de découvrir concrètement un mauvais raffinement de leur objectif. Sur la base de la classification des mauvais raffinements spécifiés, nous avons défini quatre types de mauvaises odeurs de raffinement final : Faible relation sémantique, Beaucoup de frères et sœurs, Quelques frères et sœurs et Feuille à gros grains, et a développé deux types de mesures pour les détecter : des mesures sur la structure graphique d'un modèle d'objectif et la similarité sémantique des descriptions d'objectifs. Nous avons mis en place un outil d'aide à la détection des mauvaises odeurs et évalué son utilité par une expérimentation.
Shinpei HAYASHI
Tokyo Institute of Technology
Keisuke ASANO
Tokyo Institute of Technology
Motoshi SAEKI
Nanzan University
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Shinpei HAYASHI, Keisuke ASANO, Motoshi SAEKI, "Automating Bad Smell Detection in Goal Refinement of Goal Models" in IEICE TRANSACTIONS on Information,
vol. E105-D, no. 5, pp. 837-848, May 2022, doi: 10.1587/transinf.2021KBP0006.
Abstract: Goal refinement is a crucial step in goal-oriented requirements analysis to create a goal model of high quality. Poor goal refinement leads to missing requirements and eliciting incorrect requirements as well as less comprehensiveness of produced goal models. This paper proposes a technique to automate detecting bad smells of goal refinement, symptoms of poor goal refinement. At first, to clarify bad smells, we asked subjects to discover poor goal refinement concretely. Based on the classification of the specified poor refinement, we defined four types of bad smells of goal refinement: Low Semantic Relation, Many Siblings, Few Siblings, and Coarse Grained Leaf, and developed two types of measures to detect them: measures on the graph structure of a goal model and semantic similarity of goal descriptions. We have implemented a supporting tool to detect bad smells and assessed its usefulness by an experiment.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2021KBP0006/_p
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@ARTICLE{e105-d_5_837,
author={Shinpei HAYASHI, Keisuke ASANO, Motoshi SAEKI, },
journal={IEICE TRANSACTIONS on Information},
title={Automating Bad Smell Detection in Goal Refinement of Goal Models},
year={2022},
volume={E105-D},
number={5},
pages={837-848},
abstract={Goal refinement is a crucial step in goal-oriented requirements analysis to create a goal model of high quality. Poor goal refinement leads to missing requirements and eliciting incorrect requirements as well as less comprehensiveness of produced goal models. This paper proposes a technique to automate detecting bad smells of goal refinement, symptoms of poor goal refinement. At first, to clarify bad smells, we asked subjects to discover poor goal refinement concretely. Based on the classification of the specified poor refinement, we defined four types of bad smells of goal refinement: Low Semantic Relation, Many Siblings, Few Siblings, and Coarse Grained Leaf, and developed two types of measures to detect them: measures on the graph structure of a goal model and semantic similarity of goal descriptions. We have implemented a supporting tool to detect bad smells and assessed its usefulness by an experiment.},
keywords={},
doi={10.1587/transinf.2021KBP0006},
ISSN={1745-1361},
month={May},}
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TY - JOUR
TI - Automating Bad Smell Detection in Goal Refinement of Goal Models
T2 - IEICE TRANSACTIONS on Information
SP - 837
EP - 848
AU - Shinpei HAYASHI
AU - Keisuke ASANO
AU - Motoshi SAEKI
PY - 2022
DO - 10.1587/transinf.2021KBP0006
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E105-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2022
AB - Goal refinement is a crucial step in goal-oriented requirements analysis to create a goal model of high quality. Poor goal refinement leads to missing requirements and eliciting incorrect requirements as well as less comprehensiveness of produced goal models. This paper proposes a technique to automate detecting bad smells of goal refinement, symptoms of poor goal refinement. At first, to clarify bad smells, we asked subjects to discover poor goal refinement concretely. Based on the classification of the specified poor refinement, we defined four types of bad smells of goal refinement: Low Semantic Relation, Many Siblings, Few Siblings, and Coarse Grained Leaf, and developed two types of measures to detect them: measures on the graph structure of a goal model and semantic similarity of goal descriptions. We have implemented a supporting tool to detect bad smells and assessed its usefulness by an experiment.
ER -