Automatic Program Repair (APR) is a dynamically growing field of computer science that aims to reduce the time and cost of debugging code and improve its efficiency. Fault localization (FL) is a critical component of the APR workflow and has a real impact on the success of an APR procedure. The fault localization step produces a list of potentially faulty code by calculating its level of suspiciousness, i.e., the likelihood of being faulty. In the process of calculation, a great variety of metrics can be implemented. In this thesis, we examine the effectiveness of ASTOR, a Java APR framework, with chosen FL metrics for calculating suspiciousness by conducting a controlled experiment. ASTOR is tested against the Defects4J dataset, a benchmark for APR evaluation, containing bugs from open-source projects. The most significant difference between ASTOR executions regards the tests performed on the bug Math 82, where the difference between the fastest and slowest execution was of 553,23 s (the slowest execution was 571,31 s, i.e., +3060 % to the fastest execution which was 18,08 s). The experiment showed also that the mean execution time for the cases when a plausible patch was found could differ from metric to metric.