A pragmatic approach for feature interaction detection in intelligent networks

A new pragmatic approach for feature interaction (FI) detection for intelligent networks (IN) is presented in this paper. In this approach, the causes of FI given in Bellcore and European benchmarks serve as the starting point. The necessary information for feature description is derived from these causes. This information is modelled using an object-oriented template. Features are described in terms of necessary resources and actions. The detection algorithm takes this information as input and checks for interactions between features. The algorithm consists of three parts: filtering, feature instantiation and detection. Using the filtering method, all the possible interaction-prone scenarios are produced and using the list of topologically different call combinations the actual features are instantiated. The detection algorithm is run on these actual participants of the features in order to detect interactions. We applied our approach to the feature interaction benchmarks.