Synthesis of probabilistic models for quality-of-service software engineering

An increasingly used method for the engineering of software systems with strict quality-of-service (QoS) requirements involves the synthesis and verification of probabilistic models for many alternative architectures and instantiations of system parameters. Using manual trial-and-error or simple heuristics for this task often produces suboptimal models, while the exhaustive synthesis of all possible models is typically intractable. The EvoChecker search-based software engineering approach presented in our paper addresses these limitations by employing evolutionary algorithms to automate the model synthesis process and to significantly improve its outcome. EvoChecker can be used to synthesise the Pareto-optimal set of probabilistic models associated with the QoS requirements of a system under design, and to support the selection of a suitable system architecture and configuration. EvoChecker can also be used at runtime, to drive the efficient reconfiguration of a self-adaptive software system. We evaluate EvoChecker on several variants of three systems from different application domains, and show its effectiveness and applicability.     

Model-driven design space exploration for multi-robot systems in simulation

Multi-robot systems are increasingly deployed to provide services and accomplish missions whose complexity or cost is too high for a single robot to achieve on its own. Although multi-robot systems offer increased reliability via redundancy and enable the execution of more challenging missions, engineering these systems is very complex. This complexity affects not only the architecture modelling of the robotic team but also the modelling and analysis of the collaborative intelligence enabling the team to complete its mission. Existing approaches for the development of multi-robot applications do not provide a systematic mechanism for capturing these aspects and assessing the robustness of multi-robot systems. We address this gap by introducing ATLAS, a novel model-driven approach supporting the systematic design space exploration and robustness analysis of multi-robot systems in simulation. The ATLAS domain-specific language enables modelling the architecture of the robotic team and its mission and facilitates the specification of the team’s intelligence. We evaluate ATLAS and demonstrate its effectiveness in three simulated case studies: a healthcare Turtlebot-based mission and two unmanned underwater vehicle missions developed using the Gazebo/ROS and MOOS-IvP robotic platforms, respectively.

     

Polymerization efficiency of bulk‐fill dental resin composites with different curing modes

The aim of this study was to investigate the effect of four different curing modes on the polymerization efficiency of eight bulk‐fill composites. Five specimens for each material were prepared for Vickers hardness measurements. The measurements were performed at 0 and 2 or 4 mm from the top of the surface of the specimens 24 h after photopolymerization. Statistical analysis was performed with one‐way analysis of variance and Tukey post hoc tests at a level of significance of a = 0.05 where a is the the level of significance. The light‐curing mode affected the microhardness in all depths, but this influence was material‐dependent (p_mat < 0.001), where p_mat is the probability to be affected by the material. The Vickers hardness numbers of the tested composites at 0 mm ranged from 9.32 ± 0.87 to 72.58 ± 6.52 and those of the tested composites at 4 mm ranged from 5.48 ± 0.32 to 54.34 ± 2.27. The clinician has to be aware of the technical characteristics of the materials and light‐curing units (LCUs) to use the most appropriate combination of LCU, composite material, and application technique. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016 , 133, 43392.