A Clustering Based Technique for Large Scale Prioritization during Requirements Elicitation

Abstract

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.

Aspect‐based requirements mining technique to improve prioritisation process: multi‐stakeholder perspective

“…Although, these approaches may be different but the overall goal of all these techniques is the same. Thus, modified and proposed AHP to handle the requirements of dispersed stakeholders [11 ]. The sole purpose is to improve the time complexity and to ensure the success of the software.…”

“…RP helps the development team to determine the most important and urgent requirements among all the software requirements, as desired by stakeholders. This determination is within defined constraints of time, resources and cost during all stages of software development and release planning [4, 5, 8–11 ]. RP process can be affected by uncertainties, and inconsistencies caused by missing information and stakeholders’ conflicts [11–14 ].…”

SAFFRON: A Semi-Automated Framework for Software Requirements Prioritization

“…Requirements prioritization is given importance by researchers since it helps in planning software releases in the scenario where all the requirements cannot be implemented in first release due to insufficient time and budget [8]. Prioritization also enhances software testing by reducing the probability of generating ineffective test cases based on imprecise requirements.…”

“…K-means algorithm is used in [8] to resolve rank reversal problem of large scale software prioritization. Multiple criteria are used to form clusters.…”

“…It is particularly challenging for large scale projects with large number of stakeholders since such projects would have more volatile requirements that are subject to change. It is essential to take such changes into consideration when prioritizing requirements [6]- [8].…”