Estimating the Worst-Case Execution Time (WCET) of an application is an essential task in the context of developing real-time or safety-critical software, but it is also a complex and error-prone process. Conventional approaches require at least some manual inputs from the user, such as loop bounds and infeasible path information, which are hard to obtain and can lead to unsafe results if they are incorrect. This is aggravated by the lack of a comprehensive explanation of the WCET estimate, i.e., a specific trace showing how WCET was reached. It is therefore hard to spot incorrect inputs and hard to improve the worst-case timing of the application. Meanwhile, modern processors have reached a complexity that refutes analysis and puts more and more burden on the practitioner. In this article we show how all of these issues can be significantly mitigated or even solved, if we use processors that are amenable to WCET analysis. We define and identify such processors, and then we propose an automated tool set which estimates a precise WCET without unsafe manual inputs, and also reconstructs a maximum-detail view of the WCET path that can be examined in a debugger environment. Our approach is based on Model Checking, which however is known to scale badly with growing application size. We address this issue by shifting the analysis to source code level, where source code transformations can be applied that retain the timing behavior, but reduce the complexity. Our experiments show that fast and precise estimates can be achieved with Model Checking, that its scalability can even exceed current approaches, and that new opportunities arise in the context of "timing debugging".