Abstract

Abstract
Since wireless sensor network applications are ever growing in scale and complexity, managers require strong formal guarantees that any changes done to the system can be enacted safely. This paper presents the formalisation and analysis of the semantics of policies, tiny software artefacts used to orchestrate wireless sensor network applications. The semantics of policies is formalised in terms of traces augmented with information concerning the constraints under which traces are executed. These traces are composed according to the network topology and subsequently analysed using the mCRL2 model-checking tool. The analysis allows for the detection of semantical inconsistencies that may lead to dangerous or unwanted behaviour of the application based on the policy configuration. An analysis of policies in a real-world system is provided, showing how to verify security and resource usage properties. © 2012 IEEE.

Abstract
Modern software systems must support a high degree of variability to accommodate a wide range of requirements and operating conditions. This paper introduces the Abstract Behavioural Specification (ABS) language and tool suite, a comprehensive platform for developing and analysing highly adaptable distributed concurrent software systems. The ABS language has a hybrid functional and object- oriented core, and comes with extensions that support the development of systems that are adaptable to diversified requirements, yet capable to maintain a high level of trustworthiness. Using ABS, system variability is consistently traceable from the level of requirements engineering down to object behaviour. This facilitates temporal evolution, as changes to the required set of features of a system are automatically reflected by functional adaptation of the system's behaviour. The analysis capabilities of ABS stretch from debugging, observing and simulating to resource analysis of ABS models and help ensure that a system will remain dependable throughout its evolutionary lifetime. We report on the experience of using the ABS language and the ABS tool suite in an industrial case study. © 2012 Springer-Verlag.

Abstract
Connector colouring provided an intuitive semantics of Reo connectors which lead to effective implementation techniques, first based on computing colouring tables directly, and later on encodings of colouring into constraints. One weakness of the framework is that it operates globally, giving a colouring to all primitives of the connector in lock-step, including those not involved in the interaction. This global approach limits both scalability and the available concurrency. This paper addresses these problems by introducing partiality into the connector colouring model. Partial colourings allow parts of a connector to operate independently and in isolation, increasing scalability and concurrency. © 2012 IFIP International Federation for Information Processing.

Abstract
This paper presents CoopREP, a system that provides support for fault replication of concurrent programs, based on cooperative recording and partial log combination. CoopREP employs partial recording to reduce the amount of information that a given program instance is required to store in order to support deterministic replay. This allows to substantially reduce the overhead imposed by the instrumentation of the code, but raises the problem of finding the combination of logs capable of replaying the fault. CoopREP tackles this issue by introducing several innovative statistical analysis techniques aimed at guiding the search of partial logs to be combined and used during the replay phase. CoopREP has been evaluated using both standard benchmarks for multi-threaded applications and a real-world application. The results highlight that CoopREP can successfully replay concurrency bugs involving tens of thousands of memory accesses, reducing logging overhead with respect to state of the art non-cooperative logging schemes by up to 50 times in computationally intensive applications.

Abstract
Sampling techniques play a key role in achieving efficient network measurements by reducing the amount of traffic processed while trying to maintain the accuracy of network statistical behavior estimation. Despite the evolution of current techniques regarding the correctness of network parameters estimation, the overhead associated with the volume of data involved in the sampling process is still considerable. In this context, this paper proposes a new technique for multiadaptive traffic sampling based on linear prediction, which allows to reduce significantly the traffic under analysis, keeping the representativeness of samples in capturing network behavior. A proof-of-concept, evaluating this technique for real traffic traces representing distinct traffic profiles, demonstrates the effectiveness of the proposal, outperforming classic techniques both in accuracy and data volumes processed. © 2012 Springer-Verlag.