Abstract. To prove termination of Java Bytecode (JBC) automatically, we transform JBC to finite termination graphs which represent all possible runs of the program. Afterwards, the graph can be translated into "simple" formalisms like term rewriting and existing tools can be used to prove termination of the resulting term rewrite system (TRS). In this paper we show that termination graphs indeed capture the semantics of JBC correctly. Hence, termination of the TRS resulting from the termination graph implies termination of the original JBC program.
The next generation airborne collision avoidance system, ACAS X, departs from the traditional deterministic model on which the current system, TCAS, is based. To increase robustness, ACAS X relies on probabilistic models to represent the various sources of uncertainty. The work reported in this paper identifies verification challenges for ACAS X, and studies the applicability of probabilistic verification and synthesis techniques in addressing these challenges. Due to shortcomings of off-the-shelf probabilistic analysis tools, we developed a framework that is designed to handle systems with similar characteristics as ACAS X. We describe the application of our framework to ACAS X, and the results and recommendations that our analysis produced.
We show how to automatically construct a system that satisfies a given logical specification and has an optimal average behavior with respect to a specification with ratio costs. When synthesizing a system from a logical specification, it is often the case that several different systems satisfy the specification. In this case, it is usually not easy for the user to state formally which system she prefers. Prior work proposed to rank the correct systems by adding a quantitative aspect to the specification. A desired preference relation can be expressed with (i) a quantitative language, which is a function assigning a value to every possible behavior of a system, and (ii) an environment model defining the desired optimization criteria of the system, e.g., worst-case or average-case optimal. In this paper, we show how to synthesize a system that is optimal for (i) a quantitative language given by an automaton with a ratio cost function, and (ii) an environment model given by a labeled Markov decision process. The objective of the system is to minimize the expected (ratio) costs. The solution is based on a reduction to Markov Decision Processes with ratio cost functions which do not require that the costs in the denominator are strictly positive. We find an optimal strategy for these using a fractional linear program
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