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Estimating the Worst-Case Execution Time (WCET) of an application is anessential task in the context of developing real-time or safety-criticalsoftware, but it is also a complex and error-prone process. Conventionalapproaches require at least some manual inputs from the user, such as loopbounds and infeasible path information, which are hard to obtain and can leadto unsafe results if they are incorrect. This is aggravated by the lack of acomprehensive explanation of the WCET estimate, i.e., a specific trace showinghow WCET was reached. It is therefore hard to spot incorrect inputs and hard toimprove the worst-case timing of the application. Meanwhile, modern processorshave reached a complexity that refutes analysis and puts more and more burdenon the practitioner. In this article we show how all of these issues can besignificantly mitigated or even solved, if we use processors that are amenableto WCET analysis. We define and identify such processors, and then we proposean automated tool set which estimates a precise WCET without unsafe manualinputs, and also reconstructs a maximum-detail view of the WCET path that canbe examined in a debugger environment. Our approach is based on Model Checking,which however is known to scale badly with growing application size. We addressthis issue by shifting the analysis to source code level, where source codetransformations can be applied that retain the timing behavior, but reduce thecomplexity. Our experiments show that fast and precise estimates can beachieved with Model Checking, that its scalability can even exceed currentapproaches, and that new opportunities arise in the context of "timingdebugging".

Scalable and Precise Estimation and Debugging of the Worst-Case Execution Time for Analysis-Friendly Processors