Abstract This paper discusses challenges in modeling and analyzing timing behavior in multi-core processors, particularly focusing on shared resources like caches and buses. It highlights how inter-thread interference and timing anomalies—such as cache misses on one core causing hits on another—complicate Worst-Case Execution Time (WCET) estimation. The document explores methods to address these issues, including code transformations and compile-time techniques to mitigate timing unpredictability. It... This paper discusses challenges in modeling and analyzing timing behavior in multi-core processors, particularly focusing on shared resources like caches and buses. It highlights how inter-thread interference and timing anomalies—such as cache misses on one core causing hits on another—complicate Worst-Case Execution Time (WCET) estimation. The document explores methods to address these issues, including code transformations and compile-time techniques to mitigate timing unpredictability. It also emphasizes the importance of accurate processor modeling for real-time systems and addresses limitations in existing documentation regarding verification purposes. Additionally, the paper touches upon timing anomalies in superscalar processors and proposes solutions involving binary modification and instruction injection to achieve deterministic execution behavior. Overall, it stresses the complexity of ensuring timing predictability in multicore architectures for real-time applications. Read More Read Less

Abstract The attached file presents a framework for analyzing and predicting the reliability of context-aware systems, particularly focusing on elder care applications. It integrates model checking and testing techniques to evaluate system correctness and reliability under various scenarios. The approach involves constructing a Markov Decision Process (MDP) model, performing reachability analysis, and conducting sensitivity analysis to identify critical components affecting system reliability. Experimental results demonstrate... The attached file presents a framework for analyzing and predicting the reliability of context-aware systems, particularly focusing on elder care applications. It integrates model checking and testing techniques to evaluate system correctness and reliability under various scenarios. The approach involves constructing a Markov Decision Process (MDP) model, performing reachability analysis, and conducting sensitivity analysis to identify critical components affecting system reliability. Experimental results demonstrate the effectiveness of the framework in detecting bugs and analyzing reliability distribution across components like sensors and networks. The study also highlights practical applications in real-world settings, such as monitoring activities and delivering timely reminders to users. Overall, the work provides a comprehensive method for improving the dependability of context-aware systems through formal verification and quantitative evaluation. Read More Read Less