22,059 research outputs found

    A random testing approach using pushdown automata

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    International audienceSince finite automata are in general strong abstractions of systems, many test cases which are automata traces generated uniformly at ran-dom, may be un-concretizable. This paper proposes a method extending the abovementioned testing approach to pushdown systems providing finer abstractions. Using combinatorial techniques guarantees the uniformity of generated traces. In addition, to improve the quality of the test suites, the combination of coverage criteria with random testing is investigated. The method is illustrated within both structural and model-based testing contexts

    Swarm testing

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    ManuscriptSwarm testing is a novel and inexpensive way to improve the diversity of test cases generated during random testing. Increased diversity leads to improved coverage and fault detection. In swarm testing, the usual practice of potentially including all features in every test case is abandoned. Rather, a large "swarm" of randomly generated configurations, each of which omits some features, is used, with configurations receiving equal resources. We have identified two mechanisms by which feature omission leads to better exploration of a system's state space. First, some features actively prevent the system from executing interesting behaviors; e.g., "pop" calls may prevent a stack data structure from executing a bug in its overflow detection logic. Second, even when there is no active suppression of behaviors, test features compete for space in each test, limiting the depth to which logic driven by features can be explored. Experimental results show that swarm testing increases coverage and can improve fault detection dramatically; for example, in a week of testing it found 42% more distinct ways to crash a collection of C compilers than did the heavily hand-tuned default configuration of a random tester

    Computing refactorings of state machines

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    For behavior models expressed in statechart-like formalisms, we show how to compute semantically equivalent yet structurally different models. These refactorings are defined by user-provided logical predicates that partition the system's state space and that characterize coherent parts - modes or control states-of the behavior. We embed the refactorings into an incremental development process that uses a combination of both tables and graphically represented state machines for describing system

    Segment Routing: a Comprehensive Survey of Research Activities, Standardization Efforts and Implementation Results

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    Fixed and mobile telecom operators, enterprise network operators and cloud providers strive to face the challenging demands coming from the evolution of IP networks (e.g. huge bandwidth requirements, integration of billions of devices and millions of services in the cloud). Proposed in the early 2010s, Segment Routing (SR) architecture helps face these challenging demands, and it is currently being adopted and deployed. SR architecture is based on the concept of source routing and has interesting scalability properties, as it dramatically reduces the amount of state information to be configured in the core nodes to support complex services. SR architecture was first implemented with the MPLS dataplane and then, quite recently, with the IPv6 dataplane (SRv6). IPv6 SR architecture (SRv6) has been extended from the simple steering of packets across nodes to a general network programming approach, making it very suitable for use cases such as Service Function Chaining and Network Function Virtualization. In this paper we present a tutorial and a comprehensive survey on SR technology, analyzing standardization efforts, patents, research activities and implementation results. We start with an introduction on the motivations for Segment Routing and an overview of its evolution and standardization. Then, we provide a tutorial on Segment Routing technology, with a focus on the novel SRv6 solution. We discuss the standardization efforts and the patents providing details on the most important documents and mentioning other ongoing activities. We then thoroughly analyze research activities according to a taxonomy. We have identified 8 main categories during our analysis of the current state of play: Monitoring, Traffic Engineering, Failure Recovery, Centrally Controlled Architectures, Path Encoding, Network Programming, Performance Evaluation and Miscellaneous...Comment: SUBMITTED TO IEEE COMMUNICATIONS SURVEYS & TUTORIAL

    CALVIN: A Benchmark for Language-Conditioned Policy Learning for Long-Horizon Robot Manipulation Tasks

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    General-purpose robots coexisting with humans in their environment must learn to relate human language to their perceptions and actions to be useful in a range of daily tasks. Moreover, they need to acquire a diverse repertoire of general-purpose skills that allow composing long-horizon tasks by following unconstrained language instructions. In this paper, we present CALVIN (Composing Actions from Language and Vision), an open-source simulated benchmark to learn long-horizon language-conditioned tasks. Our aim is to make it possible to develop agents that can solve many robotic manipulation tasks over a long horizon, from onboard sensors, and specified only via human language. CALVIN tasks are more complex in terms of sequence length, action space, and language than existing vision-and-language task datasets and supports flexible specification of sensor suites. We evaluate the agents in zero-shot to novel language instructions and to novel environments and objects. We show that a baseline model based on multi-context imitation learning performs poorly on CALVIN, suggesting that there is significant room for developing innovative agents that learn to relate human language to their world models with this benchmark.Comment: Accepted for publication at IEEE Robotics and Automation Letters (RAL). Code, models and dataset available at http://calvin.cs.uni-freiburg.d
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