Stress Testing of Task Deadlines: A Constraint Programming Approach

Safety-critical Real Time Embedded Systems (RTESs) are usually subject to strict timing and performance requirements that must be satisfied for the system to be deemed safe. In this paper, we use effective search strategies that aim at finding worst case scenarios with respect to deadline misses. Such scenarios can in turn be used to test the target RTES and ensure that, even under worst case conditions, it satisfies its timing requirements. Specifically, we develop a solution based on Constraint Programming (CP) to automate the generation of test cases that reveal, or are likely to, task deadline misses. We evaluate it through a comparison with a recent, state-of-the-art approach based on Genetic Algorithms (GA). In particular, we compare CP and GA in five industry-inspired case studies for efficiency, effectiveness, and scalability. Our experimental results show that, on the largest and more complex case studies, CP performs significantly better than GA. Since CP has interesting properties, such as guaranteeing complete search when there is sufficient time, and enables the definition of effective heuristics to converge faster towards optimal solutions, we conclude that our results are encouraging and suggest this is an advantageous solution for the stress testing of RTESs with respect to timing constraints