1,295 research outputs found

    Overcoming observability problems in distributed test architectures

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    This paper investigates conditions that must be satisfied by an FSM for the existence of input sequences that can be applied in a distributed test architecture without encountering controllability and observability problems and without using external coordination messages. Such conditions have two potential values. First, they can be used to determine whether we require coordination messages and thus a network that connects the testers. Second, if we wish to avoid the use of coordination messages in testing then these conditions can be seen as testability conditions that can inform the design process. Results given in this paper differ from those in the following ways. First, the conditions are strictly weaker than those in since we are less restrictive in the ways we achieve our goals. Second, only considered observability problems; we consider both controllability and observability problems. In addition, only considered a particular type of observability problem and we generalize this. Finally, we investigate the situation in which we need only add input sequences to complement a given test/checking sequence Ļ and prove that the conditions for this problem are equivalent to those for the original problem

    Using status messages in the distributed test architecture

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    If the system under test has multiple interfaces/ports and these are physically distributed then in testing we place a tester at each port. If these testers cannot directly communicate with one another and there is no global clock then we are testing in the distributed test architecture. If the distributed test architecture is used then there may be input sequences that cannot be applied in testing without introducing controllability problems. Additionally, observability problems can allow fault masking. In this paper we consider the situation in which the testers can apply a status message: an input that causes the system under test to identify its current state. We show how such a status message can be used in order to overcome controllability and observability problems

    Overcoming controllability problems in distributed testing from an input output transition system

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    This is the Pre-print version of the Article. The official published version can be accessed from the link below - Copyright @ 2012 Springer VerlagThis paper concerns the testing of a system with physically distributed interfaces, called ports, at which it interacts with its environment. We place a tester at each port and the tester at port p observes events at p only. This can lead to controllability problems, where the observations made by the tester at a port p are not sufficient for it to be able to know when to send an input. It is known that there are test objectives, such as executing a particular transition, that cannot be achieved if we restrict attention to test cases that have no controllability problems. This has led to interest in schemes where the testers at the individual ports send coordination messages to one another through an external communications network in order to overcome controllability problems. However, such approaches have largely been studied in the context of testing from a deterministic finite state machine. This paper investigates the use of coordination messages to overcome controllability problems when testing from an input output transition system and gives an algorithm for introducing sufficient messages. It also proves that the problem of minimising the number of coordination messages used is NP-hard

    Model-Based Testing for the Cloud

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    Software in the cloud is characterised by the need to be highly adaptive and continuously available. Incremental changes are applied to the deployed system and need to be tested in the field. Different configurations need to be tested. Higher quality standards regarding both functional and non-functional properties are put on those systems, as they often face large and diverse customer bases and/or are used as services from different peer service implementations. The properties of interest include interoperability, privacy, security, reliability, performance, resource use, timing constraints, service dependencies, availability, and so on. This paper discusses the state of the art in model-based testing of cloud systems. It focuses on two central aspects of the problem domain: (a) dealing with the adaptive and dynamic character of cloud software when tested with model-based testing, by developing new online and offline test strategies, and (b) dealing with the variety of modeling concerns for functional and non-functional properties, by devising a unified framework for them where this is possible. Having discussed the state of the art we identify challenges and future directions

    Microservices Security Challenges and Approaches

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    The fast-paced development cycles of microservices applications increase the probability of insufficient security tests in the development pipelines and consequent deployment of vulnerable microservices. The distribution and ephemeral of microservices create a discoverability challenge for traditional security assessment techniques, especially for microservices being dynamically launched and de-registered. To address this in applications and networks, continuous security assessments are used for vulnerability detection. Detected vulnerabilities are thereafter patched, essentially reducing the chances for security attacks. This paper illustrates the microservices architecture and its components from the security perspective. It investigates, summarizes, and highlights the microservices security-related challenges and the suggested approaches and proposals for facing them. It addresses the security impact on the different microservice architectural perspectives

    Deep Variational Reinforcement Learning for POMDPs

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    Many real-world sequential decision making problems are partially observable by nature, and the environment model is typically unknown. Consequently, there is great need for reinforcement learning methods that can tackle such problems given only a stream of incomplete and noisy observations. In this paper, we propose deep variational reinforcement learning (DVRL), which introduces an inductive bias that allows an agent to learn a generative model of the environment and perform inference in that model to effectively aggregate the available information. We develop an n-step approximation to the evidence lower bound (ELBO), allowing the model to be trained jointly with the policy. This ensures that the latent state representation is suitable for the control task. In experiments on Mountain Hike and flickering Atari we show that our method outperforms previous approaches relying on recurrent neural networks to encode the past
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