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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/2630

Title: Automatic generation of test sequences form EFSM models using evolutionary algorithms
Authors: Kalaji, AS
Hierons, RM
Swift, S
Keywords: Conformance testing
Evolutionary testing (ET)
Fitness function
Functions calls
EFSM
State machine
Test data generation
Genetic algorithms (GAs)
Publication Date: 2008
Abstract: Automated test data generation through evolutionary testing (ET) is a topic of interest to the software engineering community. While there are many ET-based techniques for automatically generating test data from code, the problem of generating test data from an extended finite state machine (EFSMs) is more complex and has received little attention. In this paper, we introduce a novel approach that addresses the problem of generating input test sequences that trigger given feasible paths in an EFSM model by employing an ET-based technique. The proposed approach expresses the problem as a search for input parameters to be applied to a set of functions to be called sequentially. In order to apply ET-based technique, a new fitness function is introduced to cope with the case when a test target involves calls to a set of transitions sequentially. We evaluate our approach empirically using five sets of randomly generated paths through two EFSM case studies: INRES and class 2 transport protocols. In the experiments, we apply two search techniques: a random and an ET-based which utilizes our new fitness function. Experimental results show that the proposed approach produces input test sequences that trigger all the feasible paths used with a success rate of 100%, however, the random technique failed in most cases with a success rate of 20.8%.
URI: http://bura.brunel.ac.uk/handle/2438/2630
Appears in Collections:B-SERC Research Papers
Information Systems and Computing
School of Information Systems, Computing and Mathematics Research Papers

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