Abstract

Abstract
The growing dependence of our society on increasingly complex software systems, makes software testing ever more important and challenging. In many domains, such as healthcare and transportation, several independent systems, forming a heterogeneous and distributed system of systems, are involved in the provisioning of endto- end services to users. However, existing testing techniques, namely in the model-based testing field, provide little tool support for properly testing such systems. Hence, in this paper, we propose an approach and a toolset architecture for automating the testing of end-to-end services in distributed and heterogeneous systems. The tester interacts with a visual modeling frontend to describe key behavioral scenarios, invoke test generation and execution, and visualize test results and coverage information back in the model. The visual modeling notation is converted to a formal notation amenable for runtime interpretation in the backend. A distributed test monitoring and control infrastructure is responsible for interacting with the components of the system under test, as test driver, monitor and stub. At the core of the toolset, a test execution engine coordinates test execution and checks the conformance of the observed execution trace with the expectations derived from the visual model. A real world example from the Ambient Assisted Living domain is presented to illustrate the approach.

Abstract
Application frameworks are a powerful technique for large-scale reuse but often very hard to learn from scratch. Although good documentation helps on reducing the learning curve, it is often found lacking, and costly, as it needs to attend different audiences with disparate learning needs. When code and documentation prove insufficient, developers turn to their network of experts. The lack of awareness about the experts, interrupting the wrong people, and experts unavailability are well known hindrances to effective collaboration. This paper presents the DRIVER platform, a collaborative learning environment for framework users to share their knowledge. It provides the documentation on a wiki, where the learning paths of the community of learners can be captured, shared, rated, and recommended, thus tapping into the collective knowledge of the community of framework users. The tool can be obtained at http://bit.ly/driverTool.

Abstract
Driving simulators require extensive road environments, with roads correctly modeled and similar to those found in real world. The modeling of extensive road environments, with the specific characteristics required by driving simulators, may result in a long time consuming process. This paper presents a procedural method to the modeling of large road environments. The proposed method can produce a road network design to populate an empty terrain and produce all the related road environment models. The terrain model can also be edited to produce well-constructed road environments. The road and terrain models are optimized to interactive visualization in real time, applying all the stet-of-art techniques like the level of detail selection. The proposed method allows modeling large road environments, with the realism and quality required to the realization of experimental work in driving simulators.

Abstract
Virtual environments for driving simulation aimed to scientific purposes require three-dimensional models of realistic road networks. The generation of these networks according to the requirements, if done manually by road design specialists, results in a time consuming task. Procedural generation of road networks comes as a solution to this problem with the creation of complete road networks definition adequate to simulation. This paper proposes a method to automatically generate an optimized definition of very large roads network, in an integrated process, from the selection of nodes in a terrain area, to the network topological definition. The human supervisor can interact with this generation process at any stage, in order to obtain custom road networks definitions. The proposed method reduces the use of specialists for preparing large road networks definitions. These definitions are suitable to integrate into a broader process to create road environments, with different road types, appropriate to conducting scientific experiments in driving simulation.

Abstract
Adaptive Cruise Control (ACC) is a system that maintains driver-selected speed and headway to a preceding vehicle. The system presents some limitations that are, in part or totally, unknown to the users. Hence, many drivers exhibit a rudimentary mental model of the system and place excessive trust in the device. As a consequence, negative effects on road safety can easily occur. However, to date, many studies conducted on ACC have comprised participants who had never used ACC previously. Therefore, there is limited knowledge of how ACC affects the driving performance of experienced users of the system. To shed light on this point, twenty-six participants, divided into two groups (ACC users and non-users) drove twice in the simulated environment (once with the ACC and once manually). During both drives, the participants experienced a critical situation (stationary vehicle stopped in the cruising lane of the highway). The results show that negative behavioural adaptations to the ACC resulted from the usage of the system with regard to the critical situation: the risk of collision during the driving with ACC was increased compared with the manual driving for both groups of drivers. Besides, the research stresses the negative large correlation between the driver's mental model of ACC operation in the critical situation and the safety margins maintained by the ACC users during the same situation. Finally, it was found that the drivers' trust in the system does not have an influence on the drivers' behaviour during the trial with the ACC.