The estimation of worst-case execution time (WCET) is a critical activity in the analysis of real-time systems. Evolutionary algorithms are frequently employed for the determination of worst-case data, used in the estimation of WCET. However, in order to employ an evolutionary algorithm, several executions of the application program are required, either on the target hardware or using its simulator. Multiple executions of the application program consume a huge amount of time. In order to reduce the huge execution time, this paper proposes the use of an adaptive surrogate model. The initial training of surrogate model is performed with a cycle-accurate simulator. The initially trained model is then used to assist the evolutionary algorithm by predicting the execution time of an application program. However, contrary to the direct training approach, the surrogate model in this paper is updated (adapted) during the evolution process. The adaptive training of a surrogate model increases its prediction accuracy and reduces the overall time. The validity of proposed methodology is illustrated with multiple sorting algorithms, extensively used in real-time systems.
The Worst-Case Execution Time (WCET) of real-time systems is mainly influenced by the program design, its execution environment and the input data. To cover the last factor in the context of WCET estimation, the objective of this work is to generate the test-data that maximize the execution times of the parallel real-time systems. In this paper, a test-data generation technique is proposed that uses Genetic Algorithms to automatically generate the input data, to be used for testing of parallel real-time systems. The proposed technique was applied to a parallel embedded application-Stringsearch. The result was an analysis that took as input the parallel program and generated the test-data that cause maximal execution times. The generated test-data showed improvements by exercising long execution times in comparison to randomly generated input data.
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