Utilizing Node Interference Method and Complex Network Centrality Metrics to Explore Requirement Change Propagation

Abstract

Requirements play very important role in the design process as they specify how stakeholder expectations will be satisfied. Requirements are frequently revised, due to iterative nature of the design process. These changes, if not properly managed, may result in financial and time losses leading to project failure due to possible undesired propagating effect. Current modeling methods for managing requirements do not offer formal reasoning necessary to manage the requirement change and its propagation. Predictive models to assist designers in making well informed decisions prior to change implementation do not exist. Based on the premise that requirement networks can be utilized to study change propagation, this research will allow for investigation of complex network metrics for predicting change throughout the design process. Requirement change prediction ability during the design process may lead to valuable knowledge in designing artifacts more efficiently by minimizing unanticipated changes due to mismanaged requirements. Two research questions (RQs) described are addressed in this paper: RQ 1: Can complex network centrality metrics of a requirement network be utilized to predict requirement change propagation? RQ 2: How does complex network centrality metrics approach perform in comparison to the previously developed Automated Requirement Change Propagation Prediction (ARCPP) tool? Applying the notion of interference, requirement nodes in which change occurs are virtually removed from the network to simulate a change scenario and the changes in values of select metrics of all other nodes are observed. Based on the amount of metric value changes the remaining nodes experience, propagated requirement nodes are predicted. Counting betweenness centrality, left eigenvector centrality, and authority centrality serve as top performing metrics and their performances are comparative to ARCPP tool.