Syndicated content in the Internet has been a huge success ever since the early days of RSS 0.9 and MyNetscape. Currently, it is the cornerstone of content push, ranging from podcasts to emerging Web 2.0 sites such as Friend-Feed and Plexus. Unfortunately, the simple technology that makes publication and subscription very simple and flexible, thus explaining in part its success, is also limiting its usefulness in more demanding applications.
This paper proposes a novel distributed architecture for feed caching and dissemination. It leverages social networks as promoters of discovery and aggregation, and peer-to-peer protocols for content distribution, while providing an evolutionary upgrade path that does not disrupt current infrastructure or require changes to publishers’ or consumers’ habits.

Recent approaches to component-based software engineering employ coordinating connectors to compose components into software systems. For maximum flexibility and reuse, such connectors can themselves be composed, resulting in an expressive calculus of connectors whose semantics encompasses complex combinations of synchronisation, mutual exclusion, non-deterministic choice and state-dependent behaviour. A more expressive notion of connector includes also context-dependent behaviour, namely, whenever the choices the connector can take change non-monotonically as the context, given by the pending activity on its ports, changes. Context dependency can express notions of priority and inhibition. Capturing context-dependent behaviour in formal models is non-trivial, as it is unclear how to propagate context in- formation through composition. In this paper we present an intuitive automata-based formal model of context-dependent connectors, and argue that it is superior to previous attempts at such a model for the coordination language Reo.

Fault diagnosis approaches can generally be categorized into spectrum-based fault localization (SFL, correlating failures with abstractions of program traces), and model-based diagnosis (MBD, logic reasoning over a behavioral model). Although MBD approaches are inherently more accurate than SFL, their high computational complexity prohibits application to large programs. We present a framework to combine the best of both worlds, coined BARINEL. The program is modeled using abstractions of program traces (as in SFL) while Bayesian reasoning is used to deduce multiple-fault candidates and their probabilities (as in MBD). A particular feature of BARINEL is the usage of a probabilistic component model that accounts for the fact that faulty components may fail intermittently. Experimental results on both synthetic and real software programs show that BARINEL typically outperforms current SFL approaches at a cost complexity that is only marginally higher. In the context of single faults this superiority is established by formal proof.

Logic reasoning approaches to fault diagnosis account for the fact that a component cjmay fail intermittently by introducing a parameter gj that ex presses the probability the component exhibits correct behavior. This component parameter gj, in conjunction with a priori fault probability, is used in a Bayesian framework to compute the posterior fault candidate probabilities. Usually, information ongjis not known a priori. While proper estimation of gjcan have a great impact on the diagnostic accuracy, at present, only approximations have been proposed. We present a novel framework, BARINEL, that computes exact estimations of gjas integral part of the posterior candidate probability computation. BARINEL’s diagnostic performance is evaluated for both synthetic and real software systems. Our results show that our approach is superior to approaches based on classical persistent fault models as well as previously proposed intermittent fault models.

Spectrum-based fault localization is a statistical technique that aims at helping software developers to find faults quickly by analyzing abstractions of program traces to create a ranking of most probable faulty components (e.g., program statements). Although spectrum-based fault localization has been shown to be effective, its diagnostic accuracy is inherently limited, since the semantics of components are not considered. In particular, components that exhibit identical execution patterns cannot be distinguished. To enhance its diagnostic quality, in this paper, we combine spectrum-based fault localization with a model-based debugging approach based on abstract interpretation within a framework coined DEPUTO. The model-based approach is used to refine the ranking obtained from the spectrum-based method by filtering out those components that do not explain the observed failures when the program’s semantics is considered. We show that this combined approach outperforms the individual approaches and other state-of-the-art automated debugging techniques.

Current automatic diagnosis techniques are predominantly of a statistical nature and, despite typical defect densities, do not explicitly consider multiple faults, as also demonstrated by the popularity of the single-fault Siemens set. We present a logic reasoning approach, called Zoltar-M(ultiple fault), that yields multiple-fault diagnoses, ranked in order of their probability. Although application of Zoltar-M to programs with many faults requires further research into heuristics to reduce computational complexity, theory as well as experiments on synthetic program models and two multiple-fault program versions from the Siemens set show that for multiple-fault programs this approach can outperform statistical techniques, notably spectrum-based fault localization (SFL). As a side-effect of this research, we present a new SFL variant, called Zoltar-S(ingle fault), that is provably optimal for single-fault programs, outperforming all other variants known to date.