Nowadays, locating software components responsible for observed failures is one of the most expensive and error-prone tasks in the software development process. To improve the debugging process efficiency, some effort was already made to automatically assist the detection and location of software faults. This led to the creation of statistical debugging tools such as Tarantula, Zoltar and GZoltar. These tools use information gathered from code coverage data and the result of test executions to return a list of potential faulty locations. Although helpful, fault localization tools have some scaling problems because of the fine-grained coverage data they need to perform the fault localization analysis. Instrumentation overhead, which in some cases can be as high as 50% is the main cause for their inefficiency. This thesis proposes a new approach to this problem, avoiding as much as possible the high level of coverage detail, while still using the proven techniques these fault localization tools employ. This approach, named DCC, consists of using a coarser initial instrumentation, obtaining only coverage traces for large components. Then, the instrumentation detail of certain components is progressively increased, based on the intermediate results provided by the same techniques employed in current fault localization tools. To assess the validity of our proposed approach, an empirical evaluation was performed, injecting faults in four real-world software projects. The empirical evaluation demonstrates that the DCC approach reduces the execution overhead that exists in spectrum-based fault localization, and even presents a more concise potential fault ranking to the user. We have observed execution time reductions of 27% on average and diagnostic report size reductions of 63% on average.