Software Features Prioritization based on Stakeholders' Satisfaction/Dissatisfaction and Hesitation

In this paper we present a practical method that can be applied to support the prioritization of large sets of candidate software features in a requirements prioritization process. We consider as prioritization criteria the satisfaction/dissatisfaction of users from offering/not offering software fe...

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Bibliographic Details
Published in2020 46th Euromicro Conference on Software Engineering and Advanced Applications (SEAA) pp. 265 - 271
Main Authors Gerogiannis, Vassilis C., Tsoni, Eftychia, Born, Christian, Iatrellis, Omiros
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2020
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Summary:In this paper we present a practical method that can be applied to support the prioritization of large sets of candidate software features in a requirements prioritization process. We consider as prioritization criteria the satisfaction/dissatisfaction of users from offering/not offering software features as part of an upcoming software release. There is often an asymmetry between users' satisfaction and dissatisfaction when these two factors are considered as prioritization criteria of features: some features may generate satisfaction to users, if included in the next software release, but do not create the same value of dissatisfaction, if they are not included, and vice versa. This asymmetry may introduce additional hesitation and uncertainty to stakeholders when they adopt satisfaction/dissatisfaction as prioritization criteria. The suggested method initially requires from stakeholders to systematically rank all candidate features based on satisfaction and dissatisfaction criteria. Then, the method is used to quantify the hesitation of stakeholders that is inherent in each features ranking. The final features' priorities are computed by calculating objective weights for all stakeholders' rankings. The method assumes the larger the hesitation (lack of knowledge and indeterminacy) associated with each stakeholder ranking, the smaller will be the weight of that ranking in the calculation of the final features' priorities.
DOI:10.1109/SEAA51224.2020.00052