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|Automated test sequence generation for finite state machines using genetic algorithms
|Derderian, Karnig Agop
|Brunel University, School of Information Systems, Computing and Mathematics
|Testing software implementations, formally specified using finite state automata (FSA) has been of interest. Such systems include communication protocols and control sections of safety critical systems. There is extensive literature regarding how to formally validate an FSM based specification, but testing that an implementation conforms to the specification is still an open problem. Two aspects of FSA based testing, both NP-hard problems, are discussed in this thesis and then combined. These are the generation of state verification sequences (UIOs) and the generation of sequences of conditional transitions that are easy to trigger. In order to facilitate test sequence generation a novel representation of the transition conditions and a number of fitness function algorithms are defined. An empirical study of the effectiveness on real FSA based systems and example FSAs provides some interesting positive results. The use of genetic algorithms (GAs) makes these problems scalable for large FSAs. The experiments used a software tool that was developed in Java.
|This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.
|Appears in Collections:
|Brunel University Theses
Software Engineering (B-SERC)
Dept of Computer Science Theses
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