Learning Operational Requirements from Goal Models

Goal-oriented methods have increasingly been recognised as an effective means for eliciting, elaborating, analysing and specifying software requirements. A key activity in these approaches is the elaboration of a correct and complete set of operational requirements, in the form, of pre- and trigger-conditions, that guarantee the system goals. Few existing approaches provide support for this crucial task and mainly rely on significant effort and expertise of the engineer. In this paper we propose a tool-based framework that combines model checking, inductive learning and scenarios for elaborating operational requirements from, goal models. This is an iterative process that requires the engineer to identify positive and negative scenarios from counterexamples to the goals, generated using model checking, and to select operational requirements from, suggestions computed by inductive learning.