Evolutionary software engineering, a review

When testing software, by using any optimization method as a test data generator, we are optimizing the given input according to a selected software metric encoded as a fitness function. The success of genetic algorithms in optimization is based on the so called building block hypothesis. Basically, the genetic algorithms do not find any solitary bug at any higher probability than pure random search. However, evolutionary algorithms adapt to the given problem, in practice this means that a genetic algorithm-based tester generates several parameter combinations that reveal minor bugs and based on this information constructs sequences that will reveal, on the average, more bugs than pure random testing.