Finding the specific location of an error during debugging can be assisted by a variety of automated methods. One such method is Spectrum-Based Fault Localization (SBFL), which is based on test execution statistics, and is a commonly used automated method for identifying errors. The core idea of SBFL is to examine the pass/fail and coverage statistics of each test case, and then assign a level of suspicion to different parts of the program, using one of many different heuristic formulas. Another approach is to use the syntactic relationship of the program elements to trace the computation path from the observed behaviour to the actual error. For example, by using Program Slicing, a subset of the program, including the error itself, can be determined which may have contributed to the computation at the observed location in the program. Both Program Slicing and Spectrum-Based Fault Localization have a wealth of literature dedicated to them individually. However, there have been efforts to combine the two techniques in an attempt to more effectively identify faults. Previous research has demonstrated that these hybrid solutions can bring together the best aspects of the underlying methods. However, much of this research has concentrated on specific SBFL and PS algorithms, and a particular type of combination is usually proposed. Our aim is to carry out a comprehensive examination of the potential of combining these two approaches for fault localization. As we believe that this area has not yet been fully explored, we hope to develop new and innovative methods as well.