In Spectrum-Based Fault Localization (SBFL), a suspicion score is calculated by using a risk evaluation formula for each program element (e.g., statement, method, or class) based on the tests coverage and their results. Then, the elements are ranked based on their scores from the most suspicious to the less. The elements with the highest scores are considered the most likely to be faulty. The final ranking list of program elements helps developers during the debugging process when seeking the source of a fault in the program under test. In this paper, we present an approach that gives more importance to program elements that are executed by more failed test cases compared to other elements. In essence, we are emphasizing the failing test cases factor because there are comparably much less failing tests than passing ones. We multiply each element’s suspicion score obtained by an SBFL formula by this importance weight, which is simply the ratio of covering failing tests over all failing tests. The proposed approach can be applied to any SBFL formula without modifying it. The experimental results of our study show that our approach achieved a better performance in terms of average ranking compared to the underlying SBFL formulas. Also, it achieved positive improvements in the Top-N categories and increased the number of cases where the faulty method became the top-ranked element.