superstate identification for state machines using search-based clustering
State machines are a popular method of representing a system at a high level of abstraction that enables developers to gain an overview of the system they represent and quickly understand it. Several techniques have been developed to reverse engineer state machines from software, so as to produce a concise and up-to-date document of how a system works. However, the machines that are recovered are usually flat and contain a large number of states. This means that the abstract picture they are supposed to provide is often itself very complex, requiring effort to understand. This paper proposes the use of search-based clustering as a means of overcoming this problem. Clustering state machines opens up the possibility of recovering the structural hierarchy of a state machine, such that superstates may be identified. An evaluation study is performed using the Bunch search-based clustering tool, which demonstrates the usefulness of the approach.
state machines search-based clustering bunch hill-climbing