The spread of computing-systems, especially the realtime embedded ones, is rapidly growing in the last years, since they find usage in numerous fields of application, including, but not limited to, industry process, critical infrastructures, transportation systems, as so forth. Indeed, in these fields, precise time-constraints hold; hence, tasks need to be correct from both the functional and temporal perspectives. As for the latter, timing behavior has to be characterized, that is usually done by exploiting either static or dynamic analysis techniques, which leverage estimations based on either a model or the actual system. In this paper, we foster an automated hybrid approach that allows characterizing the timing behavior of systems while introducing any alteration, i.e., relying on instruction-level tracing rather than code instrumentation for profiling purposes. Our approach is sensitive to the execution-context, - e.g., cache misses - and it allows re-using results from the development processes - e.g., unit tests. We considered a complex realtime application from the railway domain as a case study to evaluate our approach, empirically proving that it can provide a faithful characterization of systems in terms of worst-case execution time.