Deriving non-Zeno behaviour models from goal models using ILP

Abstract

Abstract One of the difficulties in goal-oriented requirements engineering (GORE) is the construction of behaviour models from declarative goal specifications. This paper addresses this problem using a combination of model checking and machine learning. First, a goal model is transformed into a (potentially Zeno) behaviour model. Then, via an iterative process, Zeno traces are identified by model checking the behaviour model against a time progress property, and inductive logic programming (ILP) is used to learn operational requirements ( pre-conditions ) that eliminate these traces. The process terminates giving a non-Zeno behaviour model produced from the learned pre-conditions and the given goal model.