摘要：Background: Stakeholder prioritization is one of the most significant areas of software engineering. Although several stakeholder classification models have been proposed in the literature, they have many limitations that have to be considered. Most of existing stakeholders classification models assigns the same importance weight for stakeholders classified to the same class; this is not the case in real world. Moreover, the models used to prioritize stakeholders automatically are without any methodology to previously classify stakeholders. Also, most of existing stakeholders classification models relies on manual classification. In order to overcome these limitations a novel automatic power-interest stakeholder classification and prioritization model based on rough-fuzzy hybridization method is presented. Materials and Methods: The novel automatic model proposed in this study is based on the classes presented by Mendelows model with the computer intelligent rough set theory and fuzzy logic . The rough set theory is utilized to classify stakeholders into one of 4 classes "High power/high interest, high power/low interest, low power/high interest and low power/low interest". Then, fuzzy logic is used to get the degree of importance for each stakeholder in its predetermined class. Results: The rough-fuzzy hybridization method with Mendelows model proved to be a convenient method to support the stakeholder classification and prioritization process. The output of the proposed automatic model is an accurate stakeholder prioritized list. Verification and validation processes are conducted to an updating faculty website case to ensure the correctness of the resulted stakeholder prioritized list. Also, the Weighted Score Method (WSM) is conducted to show how well the proposed model performs when comparing it with two existing models. Conclusion: A novel automatic model is proposed in this study based on Mendelows model with the computer intelligent rough set theory and fuzzy logic to overcome the existing models limitations and open up a new, accurate and highly efficient way for the stakeholders classification and prioritization process.
