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.
CITATION STYLE
Achimugu, P., Selamat, A., Azar, A. T., & Vaidyanathan, S. (2015). A hybridized approach for prioritizing software requirements based on k-means and evolutionary algorithms. Studies in Computational Intelligence, 575, 73–93. https://doi.org/10.1007/978-3-319-11017-2_4
Mendeley helps you to discover research relevant for your work.