Algorithmic optimization and parallelization of eppstein's synchronizing heuristic

Official URL: http://risc01.sabanciuniv.edu/record=b1817030 (Table of Contents)

Testing is the most expensive and time consuming phase in the development of complex systems. Model–based testing is an approach that can be used to automate the generation of high quality test suites, which is the most challenging part of testing. Formal models, such as finite state machines or automata, have been used as specifications from which the test suites can be automatically generated. The tests are applied after the system is synchronized to a particular state, which can be accomplished by using a synchronizing word. Computing a shortest synchronizing word is of interest for practical purposes, e.g. for a shorter testing time. However, computing a shortest synchronizing word is an NP–hard problem. Therefore, heuristics are used to compute short synchronizing words. GREEDY is one of the fastest synchronizing heuristics currently known. In this thesis, we present approaches to accelerate GREEDY algorithm. Firstly, we focus on parallelization of GREEDY. Second, we propose a lazy execution of the preprocessing phase of the algorithm, by postponing the preparation of the required information until it is to be used in the reset word generation phase. We suggest other algorithmic enhancements as well for the implementation of the heuristics. Our experimental results show that depending on the automata size, GREEDY can be made 500⇥ faster. The suggested improvements become more effective as the size of the automaton increases.