This article gives an overview of the, monitoring oriented programming framework (MOP). In MOP, runtime monitoring is supported and encouraged as a fundamental principle for building reliable systems. Monitors are automatically synthesized from specified properties and are used in conjunction with the original system to check its dynamic behaviors. When a specification is violated or validated at runtime, user-defined actions will be triggered, which can be any code, such as information logging or runtime recovery. Two instances of MOP are presented: JavaMOP (for Java programs) and BusMOP (for monitoring PCI bus traffic). The architecture of MOP is discussed, and an explanation of parametric trace monitoring and its implementation is given. A comprehensive evaluation of JavaMOP attests to its efficiency, especially in comparison with similar systems. The implementation of BusMOP is discussed in detail. In general, BusMOP imposes no runtime overhead on the system it is monitoring.

Abstract. The deep connection between session-typed concurrency and linear logic is embodied in the language SILL that integrates functional and message-passing concurrent programming. The exacting nature of linear typing provides strong guarantees, such as global progress, absence of deadlock, and race freedom, but it also requires explicit resource management by the programmer. This burden is alleviated in an affine type system where resources need not be used, relying on a simple form of garbage collection. In this paper we show how to effectively support both linear and affine typing in a single language, in addition to the already present unrestricted (intuitionistic) types. The approach, based on Benton's adjoint construction, suggests that the usual distinction between synchronous and asynchronous communication can be viewed through the lens of modal logic. We show how polarizing the propositions into positive and negative connectives allows us to elegantly express synchronization in the type instead of encoding it by extra-logical means.

Breakdowns in complex systems often occur as a result of system elements interacting in ways unanticipated by analysts or designers. The use of task behavior as part of a larger, formal system model is potentially useful for analyzing such problems because it allows the ramifications of different human behaviors to be verified in relation to other aspects of the system. A component of task behavior largely overlooked to date is the role of human-human interaction, particularly humanhuman communication in complex human-computer systems. We are developing a multi-method approach based on extending the Enhanced Operator Function Model language to address human agent communications (EOFMC). This approach includes analyses via theorem proving and future support for model checking linked through the EOFMC top level XML description.Herein, we consider an aviation scenario in which an air traffic controller needs a flight crew to change the heading for spacing. Although this example, at first glance, seems to be one simple task, on closer inspection we find that it involves local human-human communication, remote human-human communication, multi-party communications, communication protocols, and human-automation interaction. We show how all these varied communications can be handled within the context of EOFMC.

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Parametric properties are behavioral properties over program events that depend on one or more parameters. Parameters are bound to concrete data or objects at runtime, which makes parametric properties particularly suitable for stating multi-object relationships or protocols. Monitoring parametric properties independently of the employed formalism involves slicing traces with respect to parameter instances and sending these slices to appropriate nonparametric monitor instances. The number of such instances is theoretically unbounded and tends to be enormous in practice, to an extent that how to efficiently manage monitor instances has become one of the most challenging problems in runtime verification. The previous formalism-independent approach was only able to do the obvious, namely to garbage collect monitor instances when all bound parameter objects were garbage collected. This led to pathological behaviors where unnecessary monitor instances were kept for the entire length of a program. This paper proposes a new approach to garbage collecting monitor instances. Unnecessary monitor instances are collected lazily to avoid creating undue overhead. This lazy collection, along with some careful engineering, has resulted in RV, the most efficient parametric monitoring system to date. Our evaluation shows that the average overhead of RV in the DaCapo benchmark is 15%, which is two times lower than that of JavaMOP and orders of magnitude lower than that of Tracematches.

Parametric properties are behavioral properties over program events that depend on one or more parameters. Parameters are bound to concrete data or objects at runtime, which makes parametric properties particularly suitable for stating multi-object relationships or protocols. Monitoring parametric properties independently of the employed formalism involves slicing traces with respect to parameter instances and sending these slices to appropriate nonparametric monitor instances. The number of such instances is theoretically unbounded and tends to be enormous in practice, to an extent that how to efficiently manage monitor instances has become one of the most challenging problems in runtime verification. The previous formalism-independent approach was only able to do the obvious, namely to garbage collect monitor instances when all bound parameter objects were garbage collected. This led to pathological behaviors where unnecessary monitor instances were kept for the entire length of a program. This paper proposes a new approach to garbage collecting monitor instances. Unnecessary monitor instances are collected lazily to avoid creating undue overhead. This lazy collection, along with some careful engineering, has resulted in RV, the most efficient parametric monitoring system to date. Our evaluation shows that the average overhead of RV in the DaCapo benchmark is 15%, which is two times lower than that of JavaMOP and orders of magnitude lower than that of Tracematches.

Parametric properties are behavioral properties over program events that depend on one or more parameters. Parameters are bound to concrete data or objects at runtime, which makes parametric properties particularly suitable for stating multi-object relationships or protocols. Monitoring parametric properties independently of the employed formalism involves slicing traces with respect to parameter instances and sending these slices to appropriate nonparametric monitor instances. The number of such instances is theoretically unbounded and tends to be enormous in practice, to an extent that how to efficiently manage monitor instances has become one of the most challenging problems in runtime verification. The previous formalism-independent approach was only able to do the obvious, namely to garbage collect monitor instances when all bound parameter objects were garbage collected. This led to pathological behaviors where unnecessary monitor instances were kept for the entire length of a program. This paper proposes a new approach to garbage collecting monitor instances. Unnecessary monitor instances are collected lazily to avoid creating undue overhead. This lazy collection, along with some careful engineering, has resulted in RV, the most efficient parametric monitoring system to date. Our evaluation shows that the average overhead of RV in the DaCapo benchmark is 15%, which is two times lower than that of JavaMOP and orders of magnitude lower than that of Tracematches.

With the constant push to do more for less, the use of virtual environments (VE), simulations and serious games has exploded. Human Factors (HF) personnel often use these tools to support a range of activities, including modeling process or the effects of humans in the system; designing, testing and validating new systems and processes; training skills, procedures and techniques; and even for therapeutic activities. This session will describe and demonstrate some of the diverse uses for virtual environments (VEs) in an alternate demonstration format. The session will begin with demonstrators providing a brief description of their VE, and how they've used it to answer a critical research question or address a unique need, including a video demonstration of the VE in action. After these introductions, all demonstrations will be set up around the room, and session attendees can move around the room for direct interaction with the demonstrations. This session should provoke ideas among attendees for how VEs, simulations and serious games can help address their research, training, education, evaluation or therapeutic needs.