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The deployment of complex real-time systems with everyday increasing demands and possibilities, is a challenging task for engineers when performance and efficiency have to be maximized while cost have to be minimized at the same time. For already designed systems it became necessary to perform different modifications in order to find optimal software architecture configuration by respecting all timing constraints which are essential when speaking of real-time systems. In this work, we present a model-based approach of optimizing the execution sequence of runnables within tasks in order to reduce the system's reaction times by improving the overall signal flow duration. Hereby, a genetic optimization algorithm is used to create and evaluate multiple solutions for the runnable sequencing problem. We conclude by demonstration the efficiency of the presented approach with experimental results.
Mut zu Fehlern, um die Qualität zu steigern – Fault-Injection zur Steigerung der Zuverlässigkeit
(2013)
Genetic optimization of real-time multicore systems with respect to communication-based metrics
(2012)
Automatische Multicore-Echtzeitvalidierung – Ein Prozess für modellbasierte Softwareentwicklung
(2015)
The process of engineering models of existing real-time system components is often difficult and time consuming, especially when legacy code has to be re-used or information about the exact timing behaviour is needed. In order to tackle this reverse engineering problem, we have developed the tool CoreTAna. CoreTAna derives an AUTOSAR compliant model of a real-time system by conducting dynamic analysis using trace recordings.
Motivated by the challenge of assessing the quality of reverse engineered models of real-time software, we present a novel mathematical measure for comparing trace recordings from embedded real-time systems regarding their temporal behaviour. We also introduce a benchmark framework based on this measure, for evaluating reverse engineering tools such as CoreTAna. This considers common system architectures and also includes randomly generated systems and three systems of industrial automotive projects. Finally, an industrial case study demonstrates other use cases of our measure, such as impact analysis.