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One of the major challenges facing requirements prioritization techniques is accuracy. The issue here is lack of robust algorithms capable of avoiding a mismatch between ranked requirements and stakeholder's linguistic ratings. This problem has led many software developers in building systems that eventually fall short of user's requirements. In this chapter, we propose a new approach for prioritizing software requirements that reflect high correlations between the prioritized requirements and stakeholders' linguistic valuations. Specifically, we develop a hybridized algorithm which uses preference weights of requirements obtained from the stakeholder's linguistic ratings. Our approach was validated with a dataset known as RALIC which comprises of requirements with relative weights of stakeholders.

To avoid breach of agreement or contract in software development projects, stakeholders converge to prioritize specified requirements. This is due to the fact that, not all the specified requirements can be implemented in a single release. Therefore, prioritization is the act of rating requirements according to their relative importance by project stakeholders in order to plan for software release phases. The problem of existing prioritization techniques includes computational complexities, ranking inaccuracy and large disparities between final ranks among others. Consequently, this paper presents an improved approach for prioritizing requirements for software projects requirements with stakeholders based on the limitations of existing prioritization techniques using fuzzy multi-criteria decision-making (FMCDM) approach.

Abstract. We consider the prioritization problem in cases where the number of requirements to prioritize is large using a clustering technique. Clustering is a method used to find classes of data elements with respect to their attributes. KMeans, one of the most popular clustering algorithms, was adopted in this research. To utilize k-means algorithm for solving requirements prioritization problems, weights of attributes of requirement sets from relevant project stakeholders are required as input parameters. This paper showed that, the output of running k-means algorithm on requirement sets varies depending on the weights provided by relevant stakeholders. The proposed approach was validated using a requirement dataset known as RALIC. The results suggested that, a synthetic method with scrambled centroids is effective for prioritizing requirements using k-means clustering.