The rigorous application of static timing analysis\ud requires a large and costly amount of detail knowledge on the\ud hardware and software components of the system. Probabilistic\ud Timing Analysis has potential for reducing the weight of that\ud demand. In this paper, we present a sound measurement-based\ud probabilistic timing analysis technique based on Extreme Value\ud Theory. In all the experiments made as part of this work, the\ud timing bounds determined by our technique were less than\ud 15% pessimistic in comparison with the tightest possible bounds\ud obtainable with any probabilistic timing analysis technique.\ud As a point of interest to industrial users, our technique also\ud requires a comparatively low number of measurement runs of\ud the program under analysis; less than 650 runs were needed for\ud the benchmarks presented in this paper

In the last three decades a number of methods have been devised to find upper-bounds for the execution time of critical tasks in time-critical systems. Most of such methods aim to compute Worst-Case Execution Time (WCET) estimates, which can be used as trustworthy upper-bounds for the execution time that the analysed programs will ever take during operation. The range of analysis approaches used include static, measurementbased and probabilistic methods, as well as hybrid combinations of them. Each of those approaches delivers its results on the assumption that certain hypotheses hold on the timing behaviour of the system as well that the user is able to provide the needed input information.Often enough the trustworthiness of those methods is only adjudged on the basis of the soundness of the method itself. However, trustworthiness rests a great deal also on the viability of the assumptions that the method makes on the system and on the user's ability, and on the extent to which those assumptions hold in practice. This paper discusses the hypotheses on which the major state-of-the-art timing analyses methods rely, identifying pitfalls and challenges that cause uncertainty and reduce confidence on the computed WCET estimates. While identifying weaknesses, this paper does not wish to discredit any method but rather to increase awareness on their limitations and enable an informed selection of the technique that best fits the user needs.

Abstract-Measurement-based probabilistic timing analysis (MBPTA) computes trustworthy upper bounds to the execution time of software programs. MBPTA has the connotation, typical of measurement-based techniques, that the bounds computed with it only relate to what is observed in actual program traversals, which may not include the effective worst-case phenomena. To overcome this limitation, we propose Extended Path Coverage (EPC), a novel technique that allows extending the representativeness of the bounds computed by MBPTA. We make the observation data probabilistically path-independent by modifying the probability distribution of the observed timing behaviour so as to negatively compensate for any benefits that a basic block may draw from a path leading to it. This enables the derivation of trustworthy upper bounds to the probabilistic execution time of all paths in the program, even when the user-provided input vectors do not exercise the worst-case path. Our results confirm that using MBPTA with EPC produces fully trustworthy upper bounds with competitively small overestimation in comparison to state-of-the-art MBPTA techniques.

Probabilistic Timing Analysis (PTA) in general and its measurement-based variant called MBPTA in particular can mitigate some of the problems that impair current worst-case execution time (WCET) analysis techniques. MBPTA computes tight WCET bounds expressed as probabilistic exceedance functions, without needing much information on the hardware and software internals of the system. Classic WCET analysis has information needs that may be costly and difficult to satisfy, and their omission increases pessimism. Previous work has shown that MBPTA does well with benchmark programs. Real-world applications however place more demanding requirements on timing analysis than simple benchmarks. It is interesting to see how PTA responds to them. This paper discusses the application of MBPTA to a real avionics system and presents lessons learned in that process. © 2013 IEEE.Peer ReviewedPostprint (published version

The unabated increase in the complexity of the hardware and software components of modern embedded real-time systems has given momentum to a host of research in the use of probabilistic and statistical techniques for timing analysis. In the last few years, that front of investigation has yielded a body of scientific literature vast enough to warrant some comprehensive taxonomy of motivations, strategies of application, and directions of research. This survey addresses this very need, singling out the principal techniques in the state of the art of timing analysis that employ probabilistic reasoning at some level, building a taxonomy of them, discussing their relative merit and limitations, and the relations among them. In addition to offering a comprehensive foundation to savvy probabilistic timing analysis, this paper also identifies the key challenges to be addressed to consolidate the scientific soundness and industrial viability of this emerging field.