Hybrid analytic hierarchy process-based quantitative satisfaction propagation in goal-oriented requirements engineering through sensitivity analysis

Abstract

In the early phase of Requirements Engineering (RE), Goal-Oriented Requirements Engineering (GORE) has been found to be a valuable tool. GORE plays a vital role in requirements analysis such as alternative selection decision-making process. This is carried out to determine the practicability and effectiveness of alternative approaches to arriving at quality goals. Most GORE models handle alternative selection based on an extremely coarse-grained qualitative approach, making it impossible to distinguish two alternatives. Many proposals are based on quantitative alternative choices, yet they do not offer a clear decision-making judgement. We propose a fuzzy-based quantitative approach to perform goal analysis using inter-actor dependencies in the i* framework, thereby addressing the ambiguity problems that arise in qualitative analysis. The goal analysis in the i* framework was performed by propagating the impact and weight values throughout the entire hierarchy of an actor. In this article, the Analytic Hierarchy Process (AHP) is adapted with GORE to discuss the evaluation of alternative strategies of the i* goal model of interdependent actors. By using a quantitative requirement prioritisation method such as the AHP, weights of importance are assigned to softgoals to obtain a multi-objective optimised function. The proposed hybrid method measures the degree of contribution of alternatives to the fulfillment of top softgoals. The integration of AHP with goal anlaysis helps to measure alternative options against each other based on the requirements problem. This approach also includes the sensitivity analysis, which helps to check the system behaviour for change in input parameter. Hence, it facilitates decision-making for the benefit of the requirements’ analyst. To explain the proposed solution, this paper considers a telemedicine system case study from the existing literature.