@inproceedings{SailerDeubzerLuettgenetal., author = {Sailer, Andreas and Deubzer, Michael and L{\"u}ttgen, Gerald and Mottok, J{\"u}rgen}, title = {CoreTAna: A Trace Analyzer for Reverse Engineering Real-Time Software}, series = {IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER), March 14 to March 18 2016, Suita, Osaka, Japan}, booktitle = {IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER), March 14 to March 18 2016, Suita, Osaka, Japan}, doi = {10.1109/SANER.2016.36}, abstract = {With the availability of the AUTOSAR standard, model-driven methodologies are becoming established in theautomotive domain. However, the process of creating models ofexisting system components is often difficult and time consuming, especially when legacy code has to be re-used or informationabout the exact timing behavior is needed. In order to tackle thisreverse engineering problem, we present CoreTAna, a novel toolthat derives an AUTOSAR compliant model of a real-time systemfrom a dynamic analysis of its trace recordings. This paper givesan overview of CoreTAna's current features and discusses itsbenefits for reverse engineering.}, language = {en} } @inproceedings{SailerSchmidhuberDeubzeretal., author = {Sailer, Andreas and Schmidhuber, Stefan and Deubzer, Michael and Alfranseder, Martin and Mucha, Matthias and Mottok, J{\"u}rgen}, title = {Optimizing the task allocation step for multi-core processors within autosar}, series = {2013 International Conference on Applied Electronics, IEEE, Pilsen, Czech Republic}, booktitle = {2013 International Conference on Applied Electronics, IEEE, Pilsen, Czech Republic}, abstract = {We present a model-based optimization approach for the task allocation problem in embedded multi-core systems. The information therefore is obtained from a system description in AUTOSAR and runtime measurements of the runnables in hardware traces. Based on this an initial software partitioning of runnables to tasks is created. We then use a genetic algorithm to create and evaluate solutions to the task allocation problem. Each solution is hereby evaluated using a discrete event-based simulation, which allows the evaluation with regard to real-time properties, resource consumption and data-communication overhead. The significance of our approach is then shown in a case-study. There, we optimize the task allocation of an embedded system, whose complexity is comparable to that of an actual system, on a multi-core processor. Finally, the results of the optimization are transferred to an ECU Configuration Description to enable further development in compliance with the AUTOSAR methodology.}, language = {en} }