Abstract
The clustering stakeholder problem for system requirements selection and prioritization is considered inheritance in the area of requirements engineering. This paper utilized a method for clustering analysis approaches used in the marketing segmentation process for an appropriate number of stakeholders groups. An adapted genetic K-means algorithm for clustering stakeholders for software requirement engineering (GKA-RE) is introduced in this study. The algorithm is capable of generating the optimal number of clusters for stakeholders automatically. Thus, it is providing more quality clustering solution by allowing the initial seeds to be readjusted as needed. The proposed method is experimented on two datasets for system requirements known as RALIC datasets using a number of evaluation metrics and comparing GKA-RE with the K-means approach. The experimental results indicate the superiority of GKA-RE over K-means approach in obtaining higher values of evaluation metrics.
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Reyad, O., Dukhan, W.H., Marghny, M.H., Zanaty, E.A. (2021). Genetic K-Means Adaption Algorithm for Clustering Stakeholders in System Requirements. In: Hassanien, AE., Chang, KC., Mincong, T. (eds) Advanced Machine Learning Technologies and Applications. AMLTA 2021. Advances in Intelligent Systems and Computing, vol 1339. Springer, Cham. https://doi.org/10.1007/978-3-030-69717-4_21
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