Dynamic tainting is an important part of modern software engineer- ing research. State-of-the-art tools for debugging, bug detection and program analysis make use of this technique. Nonetheless, the research area based on dynamic tainting still has open questions, among others the automatic generation of program inputs.

My proposed work concentrates on the use of dynamic tainting for test case generation. The goal is the generation of complex and valid test inputs from scratch. Therefore, I use byte level taint information enhanced with additional static and dynamic program analysis. This information is used in an evolutionary algorithm to create new offsprings and mutations. Concretely, instead of crossing and mutating the whole input randomly, taint information can be used to define which parts of the input have to be mutated. Furthermore, the taint information may also be used to define evolutionary operators.

Eventually, the evolutionary algorithm is able to generate valid inputs for a program. Such inputs can be used together with the taint information for further program analysis, e.g. the generation of input grammars.