In this paper, we provide first results introducing the impact of both software and hardware events on the estimation of worst-case energy consumption of programs on embedded processors. We build a framework to better understand the representativeness of measurements with respect to both software and hardware events. We test this framework on execution times and energy consumption data for 5 existing benchmarks as a step towards a statistical worst-case energy consumption model.

The real-time systems community is facing the lack of benchmarks adapted to measurement-based worst-case execution time (WCET) estimators. We provide in this paper first steps towards such benchmarks by proposing them for single core microcontrollers, while we leave as future work the migration to multicore microcontrollers. The considered benchmarks are the programs of an open source drone autopilot. We conclude the paper by underlining the main difficulties of such migration.

The increased complexity of programs and processors is an important challenge that the embedded real-time systems community faces today, as it implies substancial timing variability. Processor features like pipelines or communication buses are not always completely described, while black-box programs integrated by third parties are hidden for IP reasons. This situation explains the use of statistical approaches to study the timing variability of programs. Most existing work is concentrated on the guarantees provided by positive answers to statistical tests, while our current work concerns potential algorithms based on the negative answers to these tests and their impact on the timing analysis. We introduce here one such algorithm, the Walking Kolmogorov-Smirnov test (WKS).

The increased use of statistical libraries within the real-time community is facing today the lack of appropriate libraries. Many of them exist in R, but existing Python statistical libraries are lacking, particularily with respect to Extreme Value Theory and probabilistic worst-case execution time estimation, which are indispensable tools for the analysis of real-time systems. This short paper attempts to describe and bridge this gap.