Using a Mining Frequency Patterns Model to Automate Passive Testing of Real-time Systems

Testing is one of the most widely used techniques to increase the confidence on the correctness of complex software systems. In this paper we extend our previous work on passive testing with invariants to incorporate (probabilistic) knowledge obtained from users of the system under test. In order to apply our technique, we need to obtain a set of invariants compiling the relevant properties of the system under test, and this is a difficult task. First, we present an algorithm to extract invariants from the specification without assuming any additional condition. Since the number of obtained invariants is huge we study an alternative. Based on the idea that an invariant is better than another one if it can be checked more times in the same log, we present an adaptation of the previous algorithm in order to sort sets of representative invariants.

[1]  David Taniar,et al.  On Mining Movement Pattern from Mobile Users , 2007, Int. J. Distributed Sens. Networks.

[2]  Mohamed Adel Serhani,et al.  Efficient traces' collection mechanisms for passive testing of Web Services , 2009, Inf. Softw. Technol..

[3]  Glenford J. Myers,et al.  Art of Software Testing , 1979 .

[4]  David Lee,et al.  Coping with Nondeterminism in Network Protocol Testing , 2005, TestCom.

[5]  Ana R. Cavalli,et al.  New approaches for passive testing using an Extended Finite State Machine specification , 2003, Inf. Softw. Technol..

[6]  Ana R. Cavalli,et al.  Passive testing and application to the GSM-MAP protocol , 1999, Inf. Softw. Technol..

[7]  Ana R. Cavalli,et al.  A passive testing approach based on invariants: application to the WAP , 2005, Comput. Networks.

[8]  Ismael Rodríguez,et al.  Formally comparing user and implementer model-based testing methods , 2008, 2008 IEEE International Conference on Software Testing Verification and Validation Workshop.

[9]  David Lee,et al.  Passive testing and applications to network management , 1997, Proceedings 1997 International Conference on Network Protocols.

[10]  Ian H. Witten,et al.  Weka-A Machine Learning Workbench for Data Mining , 2005, Data Mining and Knowledge Discovery Handbook.

[11]  Taneli Mielikäinen,et al.  Frequency-based views to pattern collections , 2006, Discret. Appl. Math..

[12]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[13]  Mercedes G. Merayo,et al.  Passive Testing of Timed Systems , 2008, ATVA.

[14]  Rance Cleaveland,et al.  Using formal methods to support testing , 2008 .