Model Inference Approach for Detecting Feature Interactions in Integrated Systems

Many of the formal techniques are orchestrated for interaction detection in a complex integrated solution of hardware and software components. However, the applicability of these techniques is in question, keeping in view time-to-market delivery and scarcity of available resources. The situation is even more intractable when little or no knowledge of components is provided. We introduce a novel approach of using model inference methods in the domain. We advocate that these methods can be used in detecting feature interactions among components by putting the integrated system under a systematic testing effort and extracting only “context-relevant” models. Our technique allows us to detect those interactions in the system which are normally hidden while testing the components in isolation. We apply our approach to an active problem we are facing in furnishing mobile phone services.

[1]  Greg Utas.  A Pattern Language of Feature Interaction , 1998, FIW.

[2]  Marc Aiguier,et al.  Feature Specification and Static Analysis for Interaction Resolution , 2006, FM.

[3]  Fuchun Joseph Lin,et al.  Next Generation Service Creation: Process, Methodology, and Tool Integration , 1998, FIW.

[4]  H. Singh,et al.  Methods of addressing the interactions of intelligent network services with embedded switch services , 1988, IEEE Communications Magazine.

[5]  Keqin Li,et al.  Integration Testing of Components Guided by Incremental State Machine Learning , 2006, Testing: Academic & Industrial Conference - Practice And Research Techniques (TAIC PART'06).

[6]  Evan H. Magill,et al.  The Use of Rollback to Prevent Incorrect Operation of Features in Intelligent Network Based Systems , 1998, FIW.

[7]  Pascale Le Gall,et al.  An Automatic Off-Line Feature Interaction Detection Method by Static Analysis of Specifications , 2005, FIW.

[8]  Dana Angluin,et al.  Learning Regular Sets from Queries and Counterexamples , 1987, Inf. Comput..

[9]  Mario Kolberg,et al.  Feature interaction: a critical review and considered forecast , 2003, Comput. Networks.

[10]  Mark Ryan,et al.  The feature construct for SMV: Semantics , 2000, FIW.

[11]  Kristofer Kimbler,et al.  Comprehensive Approach to Service Interaction Handling , 1998, Comput. Networks.

[12]  Alexander L. Wolf,et al.  Discovering models of software processes from event-based data , 1998, TSEM.

[13]  Michael Jackson,et al.  A component-based approach to telecommunication software , 1998, IEEE Software.

[14]  Olivier Gaudoin,et al.  Telephony Feature Validation against Eventuality Properties and Interaction Detection based on a Statistical Analysis of the Time to Service , 2005, FIW.

[15]  Robert Fish,et al.  The Touring Machine system , 1993, CACM.

[16]  Daniel Amyot,et al.  Feature-Interaction Visualisation and Resolution in an Agent Environment , 1998, FIW.

[17]  Amy P. Felty,et al.  Feature specification and automated conflict detection , 2003, TSEM.

[18]  Yow-Jian Lin,et al.  A feature-interaction benchmark for IN and beyond , 1993, IEEE Communications Magazine.

[19]  Paul J. Kühn,et al.  The Feature and Service Interaction Problem in Telecommunications Systems. A Survey , 1998, IEEE Trans. Software Eng..

[20]  J. Paul Gibson.  Towards a Feature Interaction Algebra , 1998, FIW.

[21]  José L. Balcázar,et al.  Algorithms for Learning Finite Automata from Queries: A Unified View , 1997, Advances in Algorithms, Languages, and Complexity.

[22]  Dana Angluin.  Queries revisited , 2004, Theor. Comput. Sci..

[23]  Umesh V. Vazirani,et al.  An Introduction to Computational Learning Theory , 1994 .

[24]  Evan H. Magill,et al.  Behaviour Based Run-Time Feature Interaction Detection and Resolution Approaches For Intelligent Networks , 1997 .