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Cyber-physical systems (CPS) are required to satisfy safety constraints in various application domains such as robotics, industrial manufacturing systems, and power systems. Faults and cyber attacks have been shown to cause safety violations, which can damage the system and endanger human lives. Resilient architectures have been proposed to ensure safety of CPS under such faults and attacks via methodologies including redundancy and restarting from safe operating conditions. The existing resilient architectures for CPS utilize different mechanisms to guarantee safety, and currently, there is no common framework to compare them. Moreover, the analysis and design undertaken for CPS employing one architecture is not readily extendable to another. In this article, we propose a timing-based framework for CPS employing various resilient architectures and develop a common methodology for safety analysis and computation of control policies and design parameters. Using the insight that the cyber subsystem operates in one out of a finite number of statuses, we first develop a hybrid system model that captures CPS adopting any of these architectures. Based on the hybrid system, we formulate the problem of joint computation of control policies and associated timing parameters for CPS to satisfy a given safety constraint and derive sufficient conditions for the solution. Utilizing the derived conditions, we provide an algorithm to compute control policies and timing parameters relevant to the employed architecture. We also note that our solution can be applied to a wide class of CPS with polynomial dynamics and also allows incorporation of new architectures. We verify our proposed framework by performing a case study on adaptive cruise control of vehicles. 

One of the most significant impediments to the long-term maintainability of software applications is code smells. Keeping up with the best coding practices can be difficult for software developers, which might lead to performance throttling or code maintenance concerns. As a result, it is imperative that large applications be regularly monitored for performance issues and code smells, so that these issues can be corrected promptly. Resolving code smells in software systems can be done in a variety of ways, but doing so all at once would be prohibitively expensive and can be out of budget. Prioritizing these solutions are therefore critical. The majority of current research prioritizes code smells according to the type of smell they cause. This method, however, is not sufficient because of a lack of knowledge regarding the frequency of code usage and code changeability behavior. Even the most complex programs have some components that are more important than others. Maintaining the functionality of certain parts is essential since they are often used. Identifying and correcting code smells in places that are frequently utilized and subject to rapid change should take precedence over other code smells. A novel strategy is proposed for finding frequently used and change-prone areas in a codebase by combining business logic, heat map information, and commit history analysis in this study. It examines the codebase, commits, and log files of Java applications to identify business processes, heat map graphs, and severity levels of various types of code smells and their commit history. This is done in order to present a comprehensive, efficient, and resource-friendly technique for identifying and prioritizing performance throttling with also handling code maintenance concerns.