Insufficient propagation of changes causes the majority of regression errors in heavily evolving software systems. Impact analysis of a particular change can help identify those parts of the system that also need to be investigated and potentially propagate the change. A static code analysis technique called Static Execute After can be used to automatically infer such impact sets. The method is safe and comparable in precision to more detailed analyses. At the same time it is significantly more efficient, hence we could apply it to different large industrial systems, including the open source WebKit project. We overview the benefits of the method, its existing implementations, and present our experiences in adapting the method to such a complex project. Finally, using this particular analysis on the WebKit project, we verify whether applying the method we can actually predict the required change propagation and hence reduce regression errors. We report on the properties of the resulting impact sets computed for the change history, and their relationship to the actual fixes required. We looked at actual defects provided by the regression test suite along with their fixes taken from the version control repository, and compared these fixes to the predicted impact sets computed at the changes that caused the failing tests. The results show that the method is applicable for the analysis of the system, and that the impact sets can predict the required changes in a fair amount of cases, but that there are still open issues for the improvement of the method.