We present a Monte-Carlo optimization technique for finding system behaviors that falsify a Metric Temporal Logic (MTL) property. Our approach performs a random walk over the space of system inputs guided by a robustness metric defined by the MTL property. Robustness is guiding the search for a falsifying behavior by exploring trajectories with smaller robustness values. The resulting testing framework can be applied to a wide class of Cyber-Physical Systems (CPS). We show through experiments on complex system models that using our framework can help automatically falsify properties with more consistency as compared to other means such as uniform sampling.

We present a Monte-Carlo optimization technique for finding inputs to a system that falsify a given Metric Temporal Logic (MTL) property. Our approach performs a random walk over the space of inputs guided by a robustness metric defined by the MTL property. Robustness can be used to guide our search for a falsifying trajectory by exploring trajectories with smaller robustness values. We show that the notion of robustness can be generalized to consider hybrid system trajectories. The resulting testing framework can be applied to non-linear hybrid systems with external inputs. We show through numerous experiments on complex systems that using our framework can help automatically falsify properties with more consistency as compared to other means such as uniform sampling.

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Abstract. We propose a new technique for the static analysis of concurrent programs comprised of multiple threads. In general, the problem is known to be undecidable even for programs with only two threads but where the threads communicate using CCS-style pairwise rendezvous [10]. However, in practice, a large fraction of concurrent programs can either be directly modeled as threads communicating solely using locks or can be reduced to such systems either by applying standard abstract interpretation techniques or by exploiting separation of control from data. For such a framework, we show that for the commonly occurring case of threads with nested access to locks, the problem is efficiently decidable. Our technique involves reducing the analysis of a concurrent program with multiple threads to individually analyzing augmented versions of the given threads. Thus not only yields decidability but also avoids construction of the state space of the concurrent program at hand and thus bypasses the state explosion problem making our technique scalable. We go on to show that for programs with threads that have non-nested access to locks, the static analysis problem for programs with even two threads becomes undecidable even for reachability, thus sharpening the result of [10]. As a case study, we consider the Daisy file system [1] which is a benchmark for analyzing the efficacy of different methodologies for debugging concurrent programs and show the existence of several bugs.

Abstract. In a biological cell, cellular functions and the genetic regulatory apparatus are implemented and controlled by a network of chemical reactions in which regulatory proteins can control genes that produce other regulators, which in turn control other genes. Further, the feedback pathways appear to incorporate switches that result in changes in the dynamic behavior of the cell. This paper describes a hybrid systems approach to modeling the intra-cellular network using continuous differential equations to model the feedback mechanisms and mode-switching to describe the changes in the underlying dynamics. We use two case studies to illustrate a modular approach to modeling such networks and describe the architectural and behavioral hierarchy in the underlying models. We describe these models using Charon [2], a language that allows formal description of hybrid systems. We provide preliminary simulation results that demonstrate how our approach can help biologists in their analysis of noisy genetic circuits. Finally we describe our agenda for future work that includes the development of models and simulation for stochastic hybrid systems.