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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".

The application of Model Checking to compute WCET has not been explored as much as Integer Linear Programming (ILP), primarily because model checkers fail to scale for complex programs. These programs have loops with large or unknown bounds, leading to a state space explosion that model checkers cannot handle. To overcome this, we have developed a technique, TIC, that employs slicing, loop acceleration and over-approximation on timeannotated source code, enabling Model Checking to scale better for WCET computation. Further, our approach is parametric, so that the user can make a trade-off between the tightness of WCET estimate and the analysis time.We conducted experiments on the Mälardalen benchmarks to evaluate the effect of various abstractions on the WCET estimate and analysis time. Additionally, we compared our estimates to those made by an ILP-based analyzer and found that ours were tighter for more than 30% of the examples and equal for the rest.

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One of the main difficulties in estimating the Worst Case Execution Time (WCET) at the binary level is that machine instructions do not allow inferring call contexts as precisely as source code, since compiler optimizations obfuscate control flow and type information. On the other hand, WCET estimation at source code level can be precise in tracking call contexts, but it is pessimistic for functions that are not available as source code. In this paper we propose approaches to join binary-level and source-level analyses, to get the best out of both. We present the arising problems in detail, evaluate the approaches qualitatively, and highlight their trade-offs.