Abstract
This paper briefly describes how property templates have been used to analyse and explore the interactive behaviour of a specific medical device (an IV infusion pump). It is proposed that interactive devices that satisfy properties based on the templates are easier and safer to use. The property templates act as heuristics for the development of suitable properties tailored to the details of the particular device. A mathematically based approach is used to prove that a specification of the device satisfies the properties.

Abstract
The paper describes a model that includes an explicit description of the information resources that are assumed to guide use, enabling a focus on properties of "plausible interactions". The information resources supported by an interactive system should be designed to encourage the correct use of the system. These resources signpost a user's interaction, helping to achieve desired goals. Analysing assumptions about information resource support is particularly relevant when a system is safety critical that is when interaction failure consequences could be dangerous, or walk-up-and-use where interaction failure may lead to reluctance to use with expensive consequences. The paper shows that expressing these resource constraints still provides a wider set of behaviours than would occur in practice. A resource may be more or less salient at a particular stage of the interaction and as a result potentially overlooked. For example, the resource may be accessible but not used because it does not seem relevant to the current goal. The paper describes how the resource framework can be augmented with additional information about the salience of the assumed resources. A medical device that is in common use in many hospitals is used as illustration.

Abstract
Ensuring the effectiveness factor of usability consists in ensuring that the application allows users to reach their goals and perform their tasks. One of the few means for reaching this goal relies on task analysis and proving the compatibility between the interactive application and its task models. Synergistic execution enables the validation of a system against its task model by co-executing the system and the task model and comparing the behavior of the system against what is prescribed in the model. This allows a tester to explore scenarios in order to detect deviations between the two behaviors. Manual exploration of scenarios does not guarantee a good coverage of the analysis. To address this, we resort to model based testing (MBT) techniques to automatically generate scenarios for automated synergistic execution. To achieve this, we generate, from the task model, scenarios to be co-executed over the task model and the system. During this generation step we explore the possibility of including considerations about user error in the analysis. The automation of the execution of the scenarios closes the process. We illustrate the approach with an example

Abstract

Abstract
Faced with the need to quantify software (un)reliability in the presence of faults, the semantics of state-based systems is urged to evolve towards quantified (e.g. probabilistic) nondeterminism. When one is approaching such semantics from a categorical perspective, this inevitably calls for some technical elaboration, in a monadic setting. This paper proposes that such an evolution be undertaken without sacrificing the simplicity of the original (qualitative) definitions, by keeping quantification implicit rather than explicit. The approach is a monad lifting strategy whereby, under some conditions, definitions can be preserved provided the semantics moves to another category. The technique is illustrated by showing how to introduce probabilism in an existing software component calculus, by moving to a suitable category of matrices and using linear algebra in the reasoning. The paper also addresses the problem of preserving monadic strength in the move from original to target (Kleisli) categories, a topic which bears relationship to recent studies in categorial physics.

Abstract
A key component in a distributed parallel analytical processing engine is shuffling, the distribution of data to multiple nodes such that the computation can be done in parallel. In this paper we describe the initial design of a communication middleware to support asynchronous shuffling of data among multiple processes on a distributed memory environment. The proposed middleware relies on RDMA (Remote Direct Memory Access) operations to transfer data, and provides basic operations to send and queue data on remote machines, and to retrieve this queued data. Preliminary results show that the RDMA-based middleware can provide a 75% reduction on communication costs, when compared with a traditional sockets implementation.