Study of aggregation algorithms for aggregating imprecise software requirements’ priorities

Abstract

Extensive Numerical Assignment (ENA) is a novel Requirements Prioritization Technique introduced by the authors that acknowledges the uncertain and imprecise nature of human judgment. A controlled experiment is conducted during which data are collected using ENA for the requirements assessment of university website system. The objective of this paper is to study how the imprecise data obtained from ENA can be aggregated using aggregation algorithms: Multiple Attribute Utility Theory (MAUT) and Interval Evidential Reasoning (IER) to generate requirements’ priorities in the presence of conflicting personal preferences among assessors. A simplified version of IER called Laplace Evidential Reasoning (LER) is introduced and the results are discussed. LER has the potential to emerge as a competent aggregation algorithm when compared to MAUT and IER, because of its reasonable processing requirements when compared to IER and its ability to produce rich set of outputs when compared to MAUT.