Deriving non-Zeno behaviour models from goal models using ILP

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...

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Detalles Bibliográficos
Autores principales: Alrajeh, D., Kramer, J., Russo, A., Uchitel, S.
Formato: JOUR
Materias:
Goal-oriented requirements engineering
Inductive learning
Model checking
Operational requirements
Zeno behaviour
Behaviour models
Declarative goals
Goal models
Iterative process
Machine-learning
Time progress
Engineering education
Logic programming
Models
Requirements engineering
Acceso en línea:http://hdl.handle.net/20.500.12110/paper_09345043_v22_n3-4_p217_Alrajeh
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
Descripción
Sumario: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 (preconditions) that eliminate these traces. The process terminates giving a non-Zeno behaviour model produced from the learned pre-conditions and the given goal model. BCS © 2009.

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