Abstract
In a growing number of domains, such as health-care and transportation, several independent systems, forming a heterogeneous and distributed system of systems, are involved in the provisioning of end-to-end services to users. Testing such systems, running over interconnected mobile and cloud-based platforms, is particularly important and challenging, with little support being provided by current tools. In order to assess the current state of the practice regarding the testing of distributed and heterogeneous systems (DHS) and identify opportunities and priorities for research and innovation initiatives, we conducted an exploratory survey that was responded by 147 software testing professionals that attended industry-oriented software testing conferences, and present the main results in this paper. The survey allowed us to assess the relevance of DHS in software testing practice, the most important features to be tested in DHS, the current status of test automation and tool sourcing for testing DHS, and the most desired features in test automation solutions for DHS. We expect that the results presented in the paper are of interest to researchers, tool vendors and service providers in this field.

Abstract
This paper discusses about a proposed solution to the obstacle avoidance problem in multi-robot systems when applied to active target tracking. It is explained how a nonlinear model predictive formation control (NMPFC) previously proposed solves this problem of fixed and moving obstacle avoidance. First, an approach is presented which uses potential functions as terms of the NMPFC. These terms penalize the proximity with mates and obstacles. A strategy to avoid singularity problems with the potential functions using a modified A* path planning algorithm was then introduced. Results with simulations and experiments with real robots are presented and discussed demonstrating the efficiency of the proposed approach.

Abstract

Abstract
There are several sources of error affecting the accuracy of underwater ranging using acoustic signals. These errors have a direct impact in the performance of Long Baseline (LBL) navigation system. This paper presents the results of experiments designed to characterize the most significant sources of errors in acoustic ranging. For the experiments, we use a set of acoustic devices and compare distances given by GPS differences with and acoustic ranges. We describe the experimental procedure and we process the results to provide a qualitative and quantitative analysis of the errors.

Abstract
Within the supply chain, the customer does not simply buy parts or services from suppliers, but also buys supplier capabilities, which results in quality products and services. Having a tool that handles the: Inspection as well as the Nonconformance, Complaint, Corrective Action and Concession processes is key to successfully track the supplier performance. Taking as a case study a Supplier Quality Management (SQM) currently in operation in an Original Equipment Manufacturer (OEM) for automotive industry, this paper presents a platform to support a Supplier Quality Management System (SQMS), that fits the technical specification ISO/TS 16949 requirements. This prototype is composed by a web platform and a mobile App, having flexibility and mobility as key main characteristics.

Abstract
Context-aware ubiquitous computing systems should be able to introspect the surrounding environment and adapt their behavior according to other existing systems and context changes. Although numerous ubiquitous computing systems have been developed that are aware of different types of context such as location, social situation, and available computational resources, few are aware of their computational behavior. Computational behavior introspection is common in reflective systems and can be used to improve the awareness and autonomy of ubicomp systems. In this paper, we propose a decentralized approach based on Simple Network Management Protocol (SNMP) and Universal Plug and Play (UPnP), and on state transition models to model and expose computational behavior. Typically, SNMP and UPnP are targeted to retrieve raw operational variables from managed network devices and consumer electronic devices, e.g., checking network interface bandwidth and automating device discovery and plug and play operations. We extend the use of these protocols by exposing the state of different ubicomp systems and associated state transitions statistics. This computational behavior may be collected locally or remotely from ubicomp systems that share a physical environment, and sent to a coordinator node or simply shared among ubicomp systems. We describe the implementation of this behavior awareness approach in a home health-care environment equipped with a VoIP Phone and a drug dispenser. We provide the means for exposing and using the behavior context in managing a simple home health-care setting. Our approach relies on a system state specification being provided by manufacturers. In the case where the specification is not provided, we show how it can be automatically discovered. We propose two machine learning approaches for automatic behavior discovery and evaluate them by determining the expected state graphs of our two systems (a VoIP Phone and a drug dispenser). These two approaches are also evaluated regarding the effectiveness of generated behavior graphs.