We introduce an efficient parametric model checking (ePMC) method for the analysis of reliability, performance and other quality-of-service (QoS) properties of software systems. ePMC speeds up the analysis of parametric Markov chains modelling the behaviour of software by exploiting domain-specific modelling patterns for the software components (e.g., patterns modelling the invocation of functionally-equivalent services used to jointly implement the same operation within service-based systems, or the deployment of the components of multi-tier software systems across multiple servers). To this end, ePMC precomputes closed-form expressions for key QoS properties of such patterns, and uses these expressions in the analysis of whole-system models. To evaluate ePMC, we show that its application to service-based systems and multi-tier software architectures reduces the analysis time by several orders of magnitude compared to current parametric model checking methods.

We propose a tool-supported framework to reason about requirements constraining resource distributions and devise strategies for routing essential services in a disaster-affected region. At the core of our approach is the Route Advisor for Disaster-Affected Regions (RADAR) framework that operates on high-level algebraic representations of the region, modelled as a cyber-physical system (cps) where resource distribution is carried out over an infrastructure connecting physical geographical locations. The Satisfiable-Modulo Theories (SMT) and graph-theoretic algorithms used by the framework supports disaster management decision-making during response and preparedness phases. We demonstrate our approach on a case study in disaster management and describe scenarios to illustrate the usefulness of RADAR.

Formal verification is used to establish the compliance of software and hardware systems with important classes of requirements. System compliance with functional requirements is frequently analyzed using techniques such as model checking, and theorem proving. In addition, a technique called quantitative verification supports the analysis of the reliability, performance, and other quality-of-service (QoS) properties of systems that exhibit stochastic behavior. In this paper, we extend the applicability of quantitative verification to the common scenario when the probabilities of transition between some or all states of the Markov models analyzed by the technique are unknown, but observations of these transitions are available. To this end, we introduce a theoretical framework, and a tool chain that establish confidence intervals for the QoS properties of a software system modelled as a Markov chain with uncertain transition probabilities. We use two case studies from different application domains to assess the effectiveness of the new quantitative verification technique. Our experiments show that disregarding the above source of uncertainty may significantly affect the accuracy of the verification results, leading to wrong decisions, and low-quality software systems.

We present a scalable approach to automatically re-configure evolving IEC 61499 systems for deployment onto an available set of resources. We capture system architecture and high-level configuration requirements formally, and use an efficient SMT-based constraint resolution to generate a valid system configuration. Any changes in the system architecture, configuration requirements, or resources are automatically translated into a minimal set of updated constraints, allowing a faster reconfiguration as compared to a monolithic approach where the whole system is re-configured. We show the feasibility of our approach by studying an airport baggage handling system developed using the IEC 61499 standard.

A growing number of business and safety-critical services are delivered by computer systems designed to reconfigure in response to changes in workloads, requirements and internal state. In recent work, we showed how a formal technique called continual verification can be used to ensure that such systems continue to satisfy their reliability and performance requirements as they evolve, and we presented the challenges associated with the new technique. In this paper, we address important instances of two of these challenges, namely the maintenance of up-to-date reliability models and the adoption of continual verification in engineering practice. To address the first challenge, we introduce a new method for learning the parameters of the reliability models from observations of the system behaviour. This method is capable of adapting to variations in the frequency of the available system observations, yielding faster and more accurate learning than existing solutions. To tackle the second challenge, we present a new software engineering tool that enables developers to use our adaptive learning and continual verification in the area of service-based systems, without a formal verification background and with minimal effort.

Software as a Service (SaaS) is an increasingly attractive option for delivering software functionality. Software vendors act as service providers provisioning the functionality directly via the Internet, and customers pay for access to the service on a flexible billing model such as subscription or pay-per-use. As a result, the generated revenue is difficult to analyse due to the highly dynamic nature of the customer's interaction with the service. We present the Services2Cloud framework to assist service providers in the analysis of their expected revenue based on customer subscription and service usage. Our approach is based on a formal specification of the service on offer and a concise expression of the service usage as probabilistic patterns which are interpreted as stochastic processes. Key features of our theoretical framework have been implemented within a web-based toolkit that aims to facilitate the revenue analysis process for service providers.

We present a tool-supported framework for the engineering of service-based systems (SBSs) capable of self-verifying their compliance with developer-specified reliability requirements. These self-verifying systems select their services dynamically by using a combination of continual quantitative verification and online updating of the verified models. Our framework enables the practical exploitation of recent theoretical advances in the development of self-adaptive SBSs through (a) automating the generation of the software components responsible for model updating, continual verification and service selection; and (b) employing standard SBS development processes.

Abstract A spatial object consists of data assigned to points in a space. Spatial objects, such as memory states and three dimensional graphical scenes, are diverse and ubiquitous in computing. We develop a general theory of spatial objects by modelling abstract data types of spatial objects as topological algebras of functions. One useful algebra is that of continuous functions, with operations derived from operations on space and data, and equipped with the compact-open topology. Terms are used as abstract syntax for defining spatial objects and conditional equational specifications are used for reasoning. We pose a completeness problem:Given a selection of operations on spatial objects, do the terms approximate all the spatial objects to arbitrary accuracy?We give some general methods for solving the problem and consider their application to spatial objects with real number attributes.

Markovian models are widely used to analyse quality-of-service properties of both system designs and deployed systems. Thanks to the emergence of probabilistic model checkers, this analysis can be performed with high accuracy. However, its usefulness is heavily dependent on how well the model captures the actual behaviour of the analysed system. Our work addresses this problem for a class of Markovian models termed discrete-time Markov chains (DTMCs). We propose a new Bayesian technique for learning the state transition probabilities of DTMCs based on observations of the modelled system. Unlike existing approaches, our technique weighs observations based on their age, to account for the fact that older observations are less relevant than more recent ones. A case study from the area of bioinformatics workflows demonstrates the effectiveness of the technique in scenarios where the model parameters change over time.

Article Free AccessThe process of communication in face to face vs. computerized conferences: a controlled experiment using Bales Interaction Process Analysis Share on Authors: Starr Roxanne Hiltz Upsala College Upsala CollegeView Profile , Kenneth Johnson Upsala College Upsala CollegeView Profile , Ann Marie Rabke Upsala College Upsala CollegeView Profile Authors Info & Claims ACL '80: Proceedings of the 18th annual meeting on Association for Computational LinguisticsJune 1980 Pages 75–79https://doi.org/10.3115/981436.981460Published:19 June 1980 0citation391DownloadsMetricsTotal Citations0Total Downloads391Last 12 Months37Last 6 weeks18 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF