This paper proposes a new safe measurement-based estimation method for Worst-Case Execution Time (WCET) of programs in real-time systems. The latest progress in Pattern Recognition of learning to detect unseen object classes by between-class attribute transfer has been used for automatic test-data generation in our method. Based on control flow graph partition, execution profiles of each basic block and probabilities of their executions can be extracted during program executions driven by test data. Afterwards, a critical path can be identified by calculating its execution probability among all feasible paths. With measurement for critical paths, WCET can be obtained by adding static analysis of hardware features to measurement results. The objective of this paper is not to present finished or almost finished work. Instead we hope to trigger discussion and solicit feedback from the community in order to avoid pitfalls experienced by others and to help focus our research.
['Liangliang Kong', 'Jianhui Jiang']
A Safe Measurement-Based Worst-Case Execution Time Estimation Using Automatic Test-Data Generation