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Abstract We consider the problem of statically detecting data races in periodic real-time programs that use locks, and run on a single processor platform. We propose a technique based on a small set of rules that exploits the priority, periodicity, locking, and timing information of tasks in the program. One of the key requirements is a response time analysis for such programs, and we propose an algorithm to compute this for the case of non-nested locks. We have implemented our analysis for real-time programs written in C in a tool called PePRacer and evaluated its performance on a small set of benchmarks from the literature.

Software used to monitor and control operations within an automation system is defined using domain-specific languages. Latent errors in the control code, if left undetected, can lead to unexpected system failures compromising the safety and the security of the automation system. Traditional analysis techniques are insufficient to detect such errors as they do not cater specifically to the underlying domain-specific language. However, given the diversity of different automation domains, there is no standard platform for analysis of these languages. This paper proposes a generic static analysis framework for domain-specific languages used in the automation domain. The analysis approach exhaustively detects runtime errors in control code and ensures compliance to good programming practices. These runtime errors and coding violations are checked against abstract syntax trees and control flow graphs derived from the code. Data Flow Analysis (DFA), Abstract interpretation and pattern-based matching techniques are used to identify domain specific errors and coding violations for control languages.

We present a framework for decentralized authorization for physical access control, using smart cards, where access to individual rooms is guarded by context-dependent policies that are dynamically evaluated. Policies are specified using a logical language parameterized by events. A policy analyzer converts policy specifications into equivalent executable automata and also generates initialization information about the contexts used in these policies. While the automata are stored in users' smart cards, context initialization information is disseminated in the system. We also provide a context modeling mechanism that supports construction and propagation of contexts in the system. Upon an access request, user automata are executed at the point of access in the presence of current context information. This results in an allow/deny decision. The benefit of this approach lies in resolving authorizations in a decentralized manner in situations where the solution needs to scale with increasing number of users.