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arXiv · 2507.22645

From Tracepoints to Timeliness: A Semi-Markov Framework for Predictive Runtime Analysis

Benno Bielmeier·Ralf Ramsauer·Takahiro Yoshida·Wolfgang Mauerer
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Abstract

Detecting and resolving violations of temporal constraints in real-time systems is both, time-consuming and resource-intensive, particularly in complex software environments. Measurement-based approaches are widely used during development, but often are unable to deliver reliable predictions with limited data. This paper presents a hybrid method for worst-case execution time estimation, combining lightweight runtime tracing with probabilistic modelling. Timestamped system events are used to construct a semi-Markov chain, where transitions represent empirically observed timing between events. Execution duration is interpreted as time-to-absorption in the semi-Markov chain, enabling worst-case execution time estimation with fewer assumptions and reduced overhead. Empirical results from real-time Linux systems indicate that the method captures both regular and extreme timing behaviours accurately, even from short observation periods. The model supports holistic, low-intrusion analysis across system layers and remains interpretable and adaptable for practical use.

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Benno Bielmeier, Ralf Ramsauer, Takahiro Yoshida, Wolfgang Mauerer. 2025-07-30. From Tracepoints to Timeliness: A Semi-Markov Framework for Predictive Runtime Analysis. https://arxiv.org/abs/2507.22645

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