Nighthawk: A two-level genetic-random unit test data generator

Randomized testing has been shown to be an effective method for testing software units. However, the thorough-ness of randomized unit testing varies widely according to the settings of certain parameters, such as the relative fre-quencies with which methods are called. In this paper, we describe a system which uses a genetic algorithm to find pa-rameters for randomized unit testing that optimize test cov-erage. We compare our coverage results to previous work, and report on case studies and experiments on system op-tions