Dynamic Optimization of Genetic Algorithms for Randomized Unit Testing by Using FSS

Randomized testing is an effective method for testing software units. It uses randomization for some aspects of input data selection. Thoroughness of randomized unit testing varies widely according to the settings of certain parameters, such as the relative frequencies with which methods are called. Nighthawk, a system which uses a genetic algorithm (GA) to find parameters for randomized unit testing and optimizing test coverage is used. A feature subset selection (FSS) tool is used to assess the size and content of the representations within the GA. Using that tool, we can reduce the size of the representation substantially, while still achieving most of the coverage found using the full representation. The reduced GA achieves almost the same results as the full system, but in only 10% of the time. The proposed system is we integrate FSS Learner into the genetic algorithmic level of Nighthawk. This integration is used to dynamically optimize the genetic algorithm for randomized unit testing.