Fixing bugs is a lengthy process which currently requires several manual steps to be undertaken by a developer. Bugs often cause crashes, which results in a program being abruptly exited. Reproducing a crash often takes a significant amount of time during this process, as it requires a developer to identify where the crash occurred and where the taint began, thus leading to the crash. Several tools, such as EvoCrash, STAR, and Beacon, have been created to automate this process. EvoCrash and Beacon employ an evolutionary approach; generating test cases to reproduce the crash over a number of generations and are guided by a fitness function. In contrast, STAR uses the static analysis technique, symbolic execution, to perform the reproduction. This PhD identifies several areas of work where there is currently no or little research. Objectives identified include creating a benchmark dataset, performing an empirical evaluation on fitness functions for crash reproduction, and combining both evolutionary and static approaches to reduce search spaces and increase the effectiveness of automated crash reproduction tools. If successful, this research will significantly reduce the time taken to reproduce and fix a crash, increasing the velocity and reliability of agile processes.