Spreadsheets are widely used by end users, and studies have shown that most end-user spreadsheets contain non-trivial errors. To improve end users productivity, recent research proposes the use of a model-driven engineering approach to spreadsheets. In this paper we conduct the first systematic empirical study to assess the effectiveness and efficiency of this approach. A set of spreadsheet end users worked with two different model-based spreadsheets, and we present and analyze the results achieved.

In cloud computing environments, a large number of users access data stored in highly available storage systems. To provide good performance to geographically disperse users and allow operation even in the presence of failures or network partitions, these systems often rely on optimistic replication solutions that guarantee only eventual consistency. In this scenario, it is important to be able to accurately and efficiently identify updates executed concurrently. In this paper, first we review, and expose problems with current approaches to causality tracking in optimistic replication: these either lose information about causality or do not scale, as they require replicas to maintain information that grows linearly with the number of clients or updates.Then, we propose a novel solution that fully captures causality while being very concise in that it maintains information that grows linearly only with the number of servers that register updates for a given data element, bounded by the degree of replication.

In this paper we present a compositional semantics for the channel-based coordination language Reo which enables the analysis of quality of service (QoS) properties of service compositions. For this purpose, we annotate Reo channels with stochastic delay rates and explicitly model data-arrival rates at the boundary of a connector, to capture its interaction with the services that comprise its environment. We propose Stochastic Reo automata as an extension of Reo automata, in order to compositionally derive a QoS-aware semantics for Reo. We further present a translation of Stochastic Reo automata to Continuous-Time Markov Chains (CTMCs). This translation enables us to use third- party CTMC verification tools to do an end-to-end performance analysis of service compositions.

When failures occur during software testing, automated software fault localization helps to diagnose their root causes and identify the defective components of a program to support debugging. Diagnosis is carried out by selecting test cases in such way that their pass or fail information will narrow down the set of fault candidates, and, eventually, pinpoint the root cause. An essential ingredient of effective and efficient fault localization is the knowledge about the intermittency of occurring failures, i.e., the rate at which defective components of a program will exhibit failures. In current fault localization processes, intermittency is either ignored completely, or merely estimated a posteriori as part of the diagnosis. In this paper, we study the reduction in testing and diagnosis effort when intermittency is known a priori. We deduce intermittency from testability, following the propagation-infection-execution (PIE) approach. Experiments with synthetic and real programs suggest significant improvement in the combined testing and diagnos is process. When compared to the next best technique, testability-based intermittency information reduces the average number of tests required to reach the same diagnosis quality by 55%, and provides an effort reduction for fault localization of 30% for the same testing effort.

Recent approaches to component-based software engineering employ coordinat- ing 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 a 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 information 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.

We study the set TA of infinite binary trees with nodes labelled in a semiring A from a coalgebraic perspective. We present coinductive definition and proof principles based on the fact that TA carries a final coalgebra structure. By viewing trees as formal power series, we develop a calculus where definitions are presented as behavioural differential equations. We present a general format for these equations that guarantees the existence and uniqueness of solutions. Although technically not very difficult, the resulting framework has surprisingly nice applications, which is illustrated by various concrete examples.

We study the set TA of infinite binary trees with nodes labelled in a semiring A from a coalgebraic perspective. We present coinductive definition and proof principles based on the fact that TA carries a final coalgebra structure. By viewing trees as formal power series, we develop a calculus where definitions are presented as behavioural differential equations. We present a general format for these equations that guarantees the existence and uniqueness of solutions. Although technically not very difficult, the resulting framework has surprisingly nice applications, which is illustrated by various concrete examples.