Deriving non-Zeno behaviour models from goal models using ILP

Authors:
D. Alrajeh;J. Kramer;A. Russo;S. Uchitel
Affiliations:
Imperial College London, Department of Computing, 180 Queen’s Gate, SW7 2AZ, London, UK;Imperial College London, Department of Computing, 180 Queen’s Gate, SW7 2AZ, London, UK;Imperial College London, Department of Computing, 180 Queen’s Gate, SW7 2AZ, London, UK;Imperial College London, Department of Computing, 180 Queen’s Gate, SW7 2AZ, London, UK and Universidad de Buenos Aires, Departamento de Computaciòn, FCEyN, Buenos Aires, Argentina
Venue:
Formal Aspects of Computing
Year:
2010

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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.