GRLMerger: an automatic approach for integrating GRL models

Abstract

Goal-oriented requirements engineering aims to describe both stakeholders and system goals and their relationships using goal models. Large goal models for complex systems are often constructed from sub-models describing various stakeholders’ views and context-related aspects. These goal-oriented sub-models, also called views, may exhibit overlaps and present discrepancies. Hence, integrating such views is considered a significant barrier to the construction of a unified goal model. Current approaches to merging goal models require intensive human intervention. This paper proposes an approach and a prototype tool, called GRLMerger, to integrate two GRL (Goal-oriented Requirement Language) models into one consolidated model that is correct, complete, and free from any conflict that may arise during the merging process. GRLMerger considers both syntactic and semantic aspects of the GRL models and allows analysts to merge them either interactively or in a fully automated mode. GRLMerger employs natural language processing (NLP) techniques to match intentional elements based on their semantic similarity. The proposed GRLMerger approach and tool have been validated using 12 experimental tasks derived from two case studies, exhibiting very promising performance.