How Requirements Prioritization Methods Handle Multiple Quality Dimensions: A Systematic Literature Review

Requirements prioritization methods increasingly consider multiple quality dimensions simultaneously, but the way these dimensions are aggregated determines what information reaches decision-makers: methods that combine dimensions into a single score can mask trade-offs that Pareto-based or outranking methods keep visible, and no evidence currently guides which choice is appropriate for a given project. This paper presents a systematic literature review of 56 studies of requirements prioritization methods published between 2010 and 2025, identified through a search of Scopus, IEEE Xplore, Web of Science, and ACM Digital Library and screened against explicit inclusion and exclusion criteria. The review characterizes how aggregation strategies are distributed across this literature and examines whether prior studies have empirically evaluated their effects on rank stability and decision transparency. The results show that 33 of 56 papers (58.9%) use compensatory aggregation, 16 (28.6%) produce dimensionality-preserving Pareto fronts, 6 are partially compensatory, and only 1 is non-compensatory under this classification framework, with compensatory methods accelerating in the most recent publication period as fuzzy extensions grow more sophisticated. No study in the retrieved corpus directly evaluates the effect of aggregation strategy on rank stability or decision transparency. This review provides the first systematic characterization of aggregation strategies in requirements prioritization and outlines the design of the comparison study needed to give practitioners and method designers an evidence-based basis for choosing among them.