Integrating new technology in a business environment raises many challenges such as ensuring that this technology meets stakeholder intentions and contributes to organizational business objectives. Goal-oriented models were successfully used in strategic planning, where stakeholders and system goals and their relationships are captured and analyzed. Such models are often constructed from sub-models describing individual views, representing the various stakeholders' views and context-related aspects. These sub-models, called also partial views, may exhibit overlaps and present some discrepancies. Hence, integrating such views is considered as a significant barrier towards the construction of a unified goal model, hence, hindering their adoption. The main objective of this research project is to develop and validate an approach to merge partial Goal-oriented Requirement Language (GRL) models. We plan to investigate the use of Natural Language Processing (NLP) techniques to detect semantic dependencies/similarities between GRL intentional elements of the input models. Both syntactic and semantic information are essential in the merging process. Furthermore, we will investigate the adoption of uncertainty taxonomies in order to mark the consolidated GRL model with uncertainty annotations specifying our merging confidence level.
|Effective start/end date||1/03/21 → 31/08/22|
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