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The potential cost savings from handling software errors within a development cycle, rather than subsequent cycles, has been estimated at 38.3 billion dollars. Such figures emphasize that current testing methods are inadequate, and that helping reduce software bugs and errors is an important area of research with a substantial payoff. This paper reports on research using genetic algorithms for test case generation for systems level testing, building on past work at the unit testing level. The goals of the paper are to explore the use of genetic algorithms for testing complex distributed systems, as well as to develop a framework or vocabulary of important environmental attributes that characterize complex systems failures. In addition, preliminary visualization techniques that might help software developers to understand and uncover complex systems failures are explored.