Exact schedulability analysis of limited-preemptive (or non-preemptive) real-time workloads with variable execution costs and release jitter is a notoriously difficult challenge due to the scheduling anomalies inherent in non-preemptive execution. Furthermore, the presence of self-suspending tasks is well-understood to add tremendous complications to an already difficult problem. By mapping the schedulability problem to the reachability problem in timed automata (TA), this paper provides the first exact schedulability test for this challenging model. Specifically, using TA extensions available in UPPAAL, this paper presents an exact schedulability test for sets of periodic and sporadic self-suspending tasks with fixed preemption points that are scheduled upon a multiprocessor under a global fixedpriority scheduling policy. To the best of our knowledge, this is the first exact schedulability test for non-and limited-preemptive self-suspending tasks (for both uniprocessor and multiprocessor systems), and thus also the first exact schedulability test for the special case of global non-preemptive fixed-priority scheduling (for either periodic or sporadic tasks). Additionally, the paper highlights some subtle pitfalls and limitations in existing TAbased schedulability tests for non-preemptive workloads.

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We present an automated system repair framework for cyber-physical systems. The proposed framework consists of three main steps: (1) system simulation and fault detection to generate a labeled dataset, (2) identification of the repairable temporal properties leading to the faulty behavior and (3) repairing the system to avoid the occurrence of the cause identified in the second step. We express the cause as a past time signal temporal logic (ptSTL) formula and present an efficient monotonicity-based method to synthesize a ptSTL formula from a labeled dataset. Then, in the third step, we modify the faulty system by removing all behaviors that satisfy the ptSTL formula representing the cause of the fault. We apply the framework to two rich modeling formalisms: discrete-time dynamical systems and timed automata. For both of them, we define repairable formulae, the corresponding repair procedures, and illustrate them over case studies.

The identification of a deterministic finite automaton (DFA) from labeled examples is a well-studied problem in the literature; however, prior work focuses on the identification of monolithic DFAs. Although monolithic DFAs provide accurate descriptions of systems' behavior, they lack simplicity and interpretability; moreover, they fail to capture sub-tasks realized by the system and introduce inductive biases away from the inherent decomposition of the overall task. In this paper, we present an algorithm for learning conjunctions of DFAs from labeled examples. Our approach extends an existing SAT-based method to systematically enumerate Pareto-optimal candidate solutions. We highlight the utility of our approach by integrating it with a stateof-the-art algorithm for learning DFAs from demonstrations. Our experiments show that the algorithm learns sub-tasks realized by the labeled examples, and it is scalable in the domains of interest.