COST Action IC 1402 ArVI: Runtime Verification Beyond Monitoring -- Activity Report of Working Group 1

This report presents the activities of the first working group of the COST Action ArVI, Runtime Verification beyond Monitoring. The report aims to provide an overview of some of the major core aspects involved in Runtime Verification. Runtime Verification is the field of research dedicated to the analysis of system executions. It is often seen as a discipline that studies how a system run satisfies or violates correctness properties. The report exposes a taxonomy of Runtime Verification (RV) presenting the terminology involved with the main concepts of the field. The report also develops the concept of instrumentation, the various ways to instrument systems, and the fundamental role of instrumentation in designing an RV framework. We also discuss how RV interplays with other verification techniques such as model-checking, deductive verification, model learning, testing, and runtime assertion checking. Finally, we propose challenges in monitoring quantitative and statistical data beyond detecting property violation

Framework on Economical Implication and Issues of SADU Implementation

Due to software which plays an increasingly role in everyday life, interaction betweenhumans and computers will increase in importance; therefore, the ability to support interactions forefficient re-use of experience is a major challenge for systems in the future. Trace Based Reasoningwill have a significant impact on applications sharing experience, when they are based on the web inparticular, since traces allow us to imagine several ways of interaction in systems and to combinemultiple modes of interaction in a single system. In the conducted study we aimed at developing anAssist System of Human Diagnostician (SADU), meaning that this system will have the humanknowledge and then information retrieved by interaction with humans at the SADU request

From model-driven software development processes to problem diagnoses at runtime

Following the “convention over configuration” paradigm, model-driven software development (MDSD) generates code to implement the “default” behaviour that has been specified by a template separate from the input model. On the one hand, developers can produce end-products without a full understanding of the templates; on the other hand, the tacit knowledge in the templates is subtle to diagnose when a runtime software failure occurs. Therefore, there is a gap between templates and runtime adapted models. Generalising from the concrete problematic examples in MDSD processes to a model-based problem diagnosis, the chapter presents a procedure to separate the automated fixes from those runtime gaps that require human judgments

Blink and it's done: Interactive queries on very large data

In this demonstration, we present BlinkDB, a massively parallel, sampling-based approximate query processing framework for running interactive queries on large volumes of data. The key observation in BlinkDB is that one can make reasonable decisions in the absence of perfect answers. BlinkDB extends the Hive/HDFS stack and can handle the same set of SPJA (selection, projection, join and aggregate) queries as supported by these systems. BlinkDB provides real-time answers along with statistical error guarantees, and can scale to petabytes of data and thousands of machines in a fault-tolerant manner. Our experiments using the TPC-H benchmark and on an anonymized real-world video content distribution workload from Conviva Inc. show that BlinkDB can execute a wide range of queries up to 150x faster than Hive on MapReduce and 10--150x faster than Shark (Hive on Spark) over tens of terabytes of data stored across 100 machines, all with an error of 2--10%.National Science Foundation (U.S.) (CISE Expeditions Award CCF-1139158)QUALCOMM Inc.Amazon.com (Firm)Google (Firm)SAP CorporationBlue GojiCisco Systems, Inc.Cloudera, Inc.Ericsson, Inc.General Electric CompanyHewlett-Packard CompanyIntel CorporationMarkLogic CorporationMicrosoft CorporationNetAppOracle CorporationSplunk Inc.VMware, Inc.United States. Defense Advanced Research Projects Agency (Contract FA8650-11-C-7136