Uncertainty-wise Requirements Prioritization with Search

Abstract

Requirements review is an effective technique to ensure the quality of requirements in practice, especially in safety-critical domains (e.g., avionics systems, automotive systems). In such contexts, a typical requirements review process often prioritizes requirements, due to limited time and monetary budget, by, for instance, prioritizing requirements with higher implementation cost earlier in the review process. However, such a requirement implementation cost is typically estimated by stakeholders who often lack knowledge about (future) requirements implementation scenarios, which leads to uncertainty in cost overrun. In this article, we explicitly consider such uncertainty (quantified as cost overrun probability) when prioritizing requirements based on the assumption that a requirement with higher importance, a higher number of dependencies to other requirements, and higher implementation cost will be reviewed with the higher priority. Motivated by this, we formulate four objectives for uncertainty-wise requirements prioritization: maximizing the importance of requirements, requirements dependencies, the implementation cost of requirements, and cost overrun probability. These four objectives are integrated as part of our search-based uncertainty-wise requirements prioritization approach with tool support, named as URP. We evaluated six Multi-Objective Search Algorithms (MOSAs) (i.e., NSGA-II, NSGA-III, MOCell, SPEA2, IBEA, and PAES ) together with Random Search ( RS ) using three real-world datasets (i.e., the RALIC, Word, and ReleasePlanner datasets) and 19 synthetic optimization problems. Results show that all the selected MOSAs can solve the requirements prioritization problem with significantly better performance than RS . Among them, IBEA was over 40% better than RS in terms of permutation effectiveness for the first 10% of prioritized requirements in the prioritization sequence of all three datasets. In addition, IBEA achieved the best performance in terms of the convergence of solutions, and NSGA-III performed the best when considering both the convergence and diversity of nondominated solutions.