Software testing plays a critical role in the software development lifecycle. A well- developed test strategy can effectively evaluate the correctness of a piece of software and find bugs. One of these test strategies is randomized unit testing. Randomized unit testing allows a tester to randomly generate a sequence of method calls that can cause faulty behaviours in a program (i.e. a failing test case). This thesis focuses on using Genetic Algorithms (GAs) to help make randomized unit testing more useful and easier to use. We use GAs in failing test case minimization, which can facilitate the debugging process for software testers. We also use GAs to help finding optimal input values for randomized test case generation, which acts as a foundation that can enhance the randomized test case generation process
