Modular robots are cyber-physical systems composed of elements similar in nature that collaborate for a common global goal. This paper reports on a formal modelling of modular robots using Markov Decision Processes (MDPs) supported by the PRISM tools. The novelty of our modelling approach consists in incorporating empirical data and feedback from energy-dependent components collected thanks to an instrumentation of real modular robots called Blinky Blocks. While using PRISM modules for specifying individual robots or motifs composed of Blinky Blocks in their environment, the collected data are used for probabilistic transitions and non-deterministic choice, as well as for rewards to estimate energy availability that impacts fallback behaviours and actuation success. Afterwards, PRISM allows performing model-based analysis of safety, recovery, and energy-boundness under non-deterministic choice of the environment, and a model-based simulation on scenarios of interest. Comparing the obtained simulation results to those observed on real individual robots and their motifs provides useful insights for the developers, such as anomalous consumption patterns or inter-module variability. The designed trustworthy models can then be used for designing and validating adaptation policies for modular robots.
The terms "consistency" and its counterpart, "inconsistency", are omnipresent in model-driven software engineering. Particularly during model evolution, consistency assurance is a central concern. Technically, consistency can be defined from various viewpoints. However, it is unclear how these definitions of consistency align with practitioners' mental models. Understanding how practitioners assess and respond to different kinds of consistency problems in their own minds is required for realizing successful consistency assurance. An important aspect towards this goal is the investigation of whether there is a shared mental model of consistency among an evenly experienced group of practitioners. The current state of the art gives no answer to this. In this work, we present a model dataset of 62 consistency problems based on simple, compositional models. For this dataset, we collected 452 consistency ratings from a student group and one rating per problem from an expert group. The results show that there is no common understanding, i.e., there is no shared mental model of consistency within a group or between groups. Still, common patterns in the ratings are observable, such as a preference for rating consistency on a fine-grained scale rather than as a boolean property, or that syntactic contradictions are much less relevant to ratings than subjective semantic interpretations. All developed tools, models, and collected data are openly available for future research.
