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The widespread adoption of cyber-physical systems in the safety-critical (hard real-time) domain is accompanied by a rising degree of code-reuse up to actual software product lines spanning different hardware platforms. Nevertheless, the dominant tools for static worst-case execution-time (WCET) analysis operate on individual, specific system instances at the binary level, further depending on machine-code–level annotations for precise analysis. Thus, this timing verification is neither portable nor reusable. PragMetis addresses this schism by providing an expressive source-level annotation language that enables to express context dependence at the library level using user-defined abstractions. These abstractions allow users to generically annotate context-dependent flow facts down to the granularity of individual loop contexts. We then use control-flow–relation graphs to transfer these facts to machine-code level for specific instances, even in the presence of certain compiler optimizations, thus achieving portability. Our evaluation results based on TACLeBench confirm that PragMetis's powerful expressions yield more accurate WCET bounds.

The static timing analysis of universal real-time operating systems (RTOS) with generically implemented services requires application and system-context–specific knowledge (e.g., number of currently active tasks) to bound overheads. However, due to the missing notion of OS semantics, contemporary timing analysis tools are unable to exploit such information, resulting in failing or overly pessimistic analysis. To tackle this issue, we present our System-wide WCET Analyses framework (SWAN). SWAN's heart is Platina, a parametric source-level annotation language that facilitates the expression and propagation of context information from the application over the OS down to the machine-code level. Through the expression of semantic interdependencies in a unified and reusable way, analysis pessimism is significantly reduced, as we demonstrate by case studies on FreeRTOS, Linux, and a real-world flight-control system. Just as important as our system-aware timing analysis is the tool support for its practical usability. Therefore, we augmented SWAN by a powerful interactive visualization and annotation environment. This enables developers to quickly identify context-dependent spots that require annotation and thus to cope with large implementations associated with universal RTOSs. Eventually, SWAN allows determining if a generically implemented system is real-time capable and thus timeliness is guaranteed.

Reliable and fine-grained cost-models are fundamental for real-time systems to statically predict worst-case execution time (WCET) estimates of program code in order to guarantee timeliness. Analogous considerations hold for energy-constrained systems where worst-case energy consumption (WCEC) values are mandatory to ensure meeting predefined energy budgets. These cost models are generally unavailable for commercial off-the-shelf (COTS) hardware platforms, although static worst-case analysis tools require those models in order to predict the WCET as well as the WCEC of program code. To solve this problem, we present NEO, an end-to-end toolchain to automate cost-model generation for both WCET and WCEC analyses. NEO exploits automatically generated benchmarks, which are input for 1) an instruction-level emulation and 2) automatically conducted execution-time and energy-consumption measurements on the target platform. The gathered values (i.e., occurrences per instruction, execution-time and energy-consumption per benchmark) are combined as mathematical optimization problems. The solutions to the formulated problems, which are designed to reveal the worst-case behavior, yield the respective cost models. To statically determine upper bounds of benchmarks, we integrated the cost models into the state-of-the-art WCET analyzer PLATIN. Our evaluations on COTS hardware reveal that our open-source, end-to-end toolchain NEO yields accurate worst-case bounds.

Being able to comprehensively evaluate the individual strengths and weaknesses of worst-case execution time (WCET) analysis tools through benchmarking is essential for improving their accuracy. Unfortunately, a lack of knowledge about the detailed characteristics, actual complexities, and internal structures of existing benchmarks often prevents finegrained assessments, and sometimes even results in misleading conclusions. In this paper we present GENE, a tool that addresses these problems by automatically generating WCET benchmarks with known properties and predefined complexities. Due to the WCETs of benchmarks created by GENE being available, this approach for example makes it possible to precisely determine the accuracy of a WCET analyzer. In addition, the fact that GENE controls the program patterns of a benchmark enables fine-grained evaluations of the particular abilities and deficiencies of different WCET analyzers, as we demonstrate for aiT and PLATIN using multiple hardware platforms.