Handling knowledge uncertainty in risk-based requirements engineering

Requirements engineers are faced with multiple sources of uncertainty. In particular, the extent to which the identified software requirements and environment assumptions are adequate and sufficiently complete is uncertain; the extent to which they will be satisfied in the system-to-be is uncertain; and the extent to which obstacles to their satisfaction will occur is uncertain. The resolution of such domain-level uncertainty requires estimations of the likelihood that those different types of situations may or may not occur. However, the extent to which the resulting estimates are accurate is uncertain as well. This meta-level uncertainty limits current risk-based methods for requirements engineering. The paper introduces a quantitative approach for managing it. An earlier formal framework for probabilistic goals and obstacles is extended to explicitly cope with uncertainties about estimates of likelihoods of fine-grained obstacles to goal satisfaction. Such estimates are elicited from multiple sources and combined in order to reduce their uncertainty margins. The combined estimates and their uncertainties are up-propagated through obstacle refinement trees and then through the system's goal model. Two metrics are introduced for measuring problematic uncertainties. When applied to the probability distributions obtained by up-propagation to the top-level goals, the metrics allow critical leaf obstacles with most problematic uncertainty margins to be highlighted. The proposed approach is evaluated on excerpts from a real ambulance dispatching system