High Volume Software Testing using Genetic Algorithms

The potential cost savings from handling software errors within a development cycle, rather than the subsequent cycles, has been estimated at nearly 40 billion dollars by the National Institute of Standards and Technology. This figure emphasizes that current testing methods are often inadequate, and that helping reduce software bugs and errors is an important area of research with a substantial payoff. This is particularly true for the increasingly complex, distributed systems used in many applications from embedded control systems to military command and control systems. These systems may exhibit intermittent or transient errors after prolonged execution that are very difficult to diagnose. This paper explores strategies that combine automated test suite generation techniques with high volume or long sequence testing. Long sequence testing repeats test cases many times, simulating extended execution intervals. These testing techniques have been found useful for uncovering errors resulting from component coordination problems, as well as system resource consumption (e.g. memory leaks) or corruption. Coupling automated test suite generation with long sequence testing could make this approach more scalable and effective in the field.