Automating Bad Smell Detection in Goal Refinement of Goal Models

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Published by the IEICE: IEICE Transactions Online.

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.

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This work builds on

• International Conference Detecting Bad Smells of Refinement in Goal-Oriented Requirements Analysis MReBa 2017

BibTeX

@article{hayashi-ieicet202205,
    author = {Shinpei Hayashi and Keisuke Asano and Motoshi Saeki},
    title = {Automating Bad Smell Detection in Goal Refinement of Goal Models},
    journal = {IEICE Transactions on Information and Systems},
    volume = {105-D},
    number = 5,
    pages = {837--848},
    doi = {10.1587/transinf.2021KBP0006},
    year = 2022,
}