Micro-controllers such as Arduino are widely used by all kinds of makers worldwide. Popularity has been driven by Arduino's simplicity of use and the large number of sensors and libraries available to extend the basic capabilities of these controllers. The last decade has witnessed a surge of software engineering solutions for "the Internet of Things", but in several cases these solutions require computational resources that are more advanced than simple, resource-limited micro-controllers.Surprisingly, in spite of being the basic ingredients of complex hardware-software systems, there does not seem to be a simple and flexible way to (1) extend the basic capabilities of micro-controllers, and (2) to coordinate inter-connected micro-controllers in "the Internet of Things". Indeed, new capabilities are added on a per-application basis and interactions are mainly limited to bespoke, point-to-point protocols that target the hardware I/O rather than the services provided by this hardware.In this paper we present the Arduino Service Interface Programming (ASIP) model, a new model that addresses the issues above by (1) providing a "Service" abstraction to easily add new capabilities to micro-controllers, and (2) providing support for networked boards using a range of strategies, including socket connections, bridging devices, MQTT-based publish-subscribe messaging, discovery services, etc. We provide an open-source implementation of the code running on Arduino boards and client libraries in Java, Python, Racket and Erlang. We show how ASIP enables the rapid development of non-trivial applications (coordination of input/output on distributed boards and implementation of a line-following algorithm for a remote robot) and we assess the performance of ASIP in several ways, both quantitative and qualitative.
Abstract. Model checking is an established technique for automatically verifying that a model satisfies a given temporal property. When the model violates the property, the model checker returns a counterexample, which is a sequence of actions leading to a state where the property is not satisfied. Understanding this counterexample for debugging the specification is a complicated task for several reasons: (i) the counterexample can contain hundreds of actions, (ii) the debugging task is mostly achieved manually, and (iii) the counterexample does not give any clue on the state of the system (e.g., parallelism or data expressions) when the error occurs. This paper presents a new approach that improves the usability of model checking by simplifying the comprehension of counterexamples. Our solution aims at keeping only actions in counterexamples that are relevant for debugging purposes. To do so, we first extract in the model all the counterexamples. Second, we define an analysis algorithm that identifies actions that make the behaviour skip from incorrect to correct behaviours, making these actions relevant from a debugging perspective. Our approach is fully automated by a tool that we implemented and applied on real-world case studies from various application areas for evaluation purposes.
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