Abstract
This paper studies complex coordination mechanisms based on constraint satisfaction. In particular, it focuses on data-sensitive connectors from the Reo coordination language. These connectors restrict how and where data can flow between loosely-coupled components taking into account the data being exchanged. Existing engines for Reo provide a very limited support for data-sensitive connectors, even though data constraints are captured by the original semantic models for Reo. When executing data-sensitive connectors, coordination constraints are not exhaustively solved at compile time but at runtime on a per-need basis, powered by an existing SMT (satisfiability modulo theories) solver. To deal with a wider range of data types and operations, we abstract data and reduce the original constraint satisfaction problem to a SAT problem, based on a variation of predicate abstraction. We show soundness and completeness of the abstraction mechanism for well-defined constraints, and validate our approach by evaluating the performance of a prototype implementation with different test cases, with and without abstraction. © Springer-Verlag Berlin Heidelberg 2013.

Abstract
Dynamic software product lines (DSPLs) combine the advantages of traditional SPLs, such as an explicit variability model connected to an integrated repository of reusable code artefacts, with the ability to exploit a system's variability at runtime. When a system needs to adapt, for example to changes in operational environment or functional requirements, DSPL systems are capable of adapting their behaviour dynamically, thus avoiding the need to halt, recompile and redeploy. The field of DSPL engineering is still in formation and general-purpose DSPL development languages and tools are rare. In this paper we introduce a language and execution environment for developing and running dynamic SPLs. Our work builds on ABS, a language and integrated development environment with dedicated support for implementing static software product lines. Our ABS extension advances the scope of ABS to dynamic SPL engineering. Systems developed using ABS are compiled to Java, and are thus executable on a wide range of platforms. Copyright 2013 ACM.

Abstract
Deterministic replay tools are a useful asset when it comes to pinpoint hard-to-reproduce bugs. However, no sweet spot has yet been found with respect to the trade-off between recording overhead and bug reproducibility, especially in the context of search-based deterministic replay techniques, which rely on inference mechanisms. In this paper, we argue that tracing the locking order, along with the local control-flow path affected by shared variables, allows to dramatically reduce the inference time to find a fault-inducing trace, while imposing only a slight increase in the overhead during production runs. Preliminary evaluation with a micro-benchmark and third-party benchmarks provides initial evidence that supports our claim. © 2013 ACM.

Abstract

Abstract
Traffic Sampling is a crucial step towards scalable network measurements, enclosing manifold challenges. The wide variety of foreseeable sampling scenarios demands for a modular view of sampling components and features, grounded on a consistent architecture. Articulating the measurement scope, the required information model and the adequate sampling strategy is a major design issue for achieving an encompassing and efficient sampling solution. This is the main focus of the present work, where a layered architecture, a taxonomy of existing sampling techniques distinguishing their inner characteristics and a flexible framework able to combine these characteristics are introduced. In addition, a new multiadaptive technique proposal, based on linear prediction, allows to reduce the measurement overhead significantly, while assuring that traffic samples reflect the statistical behavior of the global traffic under analysis.

Abstract
Traffic Sampling is a crucial step towards scalable network measurements, enclosing manifold challenges. The wide variety of foreseeable sampling scenarios demands for a modular view of sampling components and features, grounded on a consistent architecture. Articulating the measurement scope, the required information model and the adequate sampling strategy is a major design issue for achieving an encompassing and efficient sampling solution. This is the main focus of the present work, where a layered architecture, a taxonomy of existing sampling techniques distinguishing their inner characteristics and a flexible framework able to combine these characteristics are introduced. In addition, a new multiadaptive technique proposal, based on linear prediction, allows to reduce the measurement overhead significantly, while assuring that traffic samples reflect the statistical behavior of the global traffic under analysis.