Over the last 15 years Runtime Verification (RV) has grown into a diverse and active field, which has stimulated the development of numerous theoretical frameworks and tools. Many of the tools are at first sight very different and challenging to compare. Yet, there are similarities. In this work, we classify RV tools within a high-level taxonomy of concepts. We first present this taxonomy and discuss the different dimensions. Then, we survey RV tools and classify them according to the taxonomy. This paper constitutes a snapshot of the current state of the art and enables a comparison of existing tools. 1 Introduction Runtime Verification (RV) [7, 28, 29, 38] (or runtime monitoring) is (broadly) the study of methods to analyze the dynamic behavior of computational systems. The most typical analysis is to check whether a given run of a system satisfies a given specification and it is this general setting (and its variants) that we consider in this paper. Whilst topics such as specification mining or trace visualization are generally considered to be within this broad field, we do not include them in our discussion. This paper presents a taxonomy of RV frameworks and tools and uses this to classify 20 selected tools. This work is timely for a number of reasons. Firstly, after more than 15 years of maturing, the field has reached a point where such a general view is needed. The last significant attempt at a taxonomy was in 2004 [24] and had a distinctly different focus to our own. Secondly, a number of activities, such as the runtime verification competitions [4, 6, 30, 48], the RV-CuBES workshop [46, 49], two schools dedicated to RV [16], and a COST action [1] (including the development of a tutorial book on the topic [5]), have put the development of runtime verification tools into focus. Terminology. The field of RV is broad and the used terminology is not yet unified. For the sake of clarity, let us fix the following terms:-Monitored system. The system consisting of software, hardware, or a combination of the two, that is being monitored. Its behavior is usually abstracted as a trace object. The authors warmly thank Martin Leucker for the early discussions on the taxonomy and mind map representation. This article is based upon work from COST Action ARVI IC1402, supported by COST (European Cooperation in Science and Technology). In particular, the taxonomy and classification benefited from discussions within working groups one and two of this action. We would also like to acknowledge input from participants of Dagstuhl seminar 17462 [34].

Modern complex software systems produce a large amount of execution data, often stored in logs. These logs can be analyzed using trace checking techniques to check whether the system complies with its requirements specifications. Often these specifications express quantitative properties of the system, which include timing constraints as well as higher-level constraints on the occurrences of significant events, expressed using aggregate operators.In this paper we present an algorithm that exploits the MapReduce programming model to check specifications expressed in a metric temporal logic with aggregating modalities, over large execution traces. The algorithm exploits the structure of the formula to parallelize the evaluation, with a significant gain in time. We report on the assessment of the implementation-based on the Hadoop framework-of the proposed algorithm and comment on its scalability.

Cloud-based elastic systems run on a cloud infrastructure and have the capability of dynamically adjusting the allocation of their resources in response to changes in the workload, in a way that balances the trade-off between the desired quality-of-service and the operational costs. The actual elastic behavior of these systems is determined by a combination of factors, including the input workload, the logic of the elastic controller determining the type of resource adjustment, and the underlying technological platform implementing the cloud infrastructure. All these factors have to be taken into account to express the desired elastic behavior of a system, as well as to verify whether the system manifests or not such a behavior.In this paper, we take a first step into these directions, by proposing a formalization, based on the CLTL t (D) temporal logic, of several concepts and properties related to the behavior of cloud-based elastic systems. We also report on our preliminary evaluation of the feasibility to check the (formalized) properties on execution traces using an automated verification tool.

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Runtime verification is an area of formal methods that studies the dynamic analysis of execution traces against formal specifications. Typically, the two main activities in runtime verification efforts are the process of creating monitors from specifications, and the algorithms for the evaluation of traces against the generated monitors. Other activities involve the instrumentation of the system to generate the trace and the communication between the system under analysis and the monitor.Most of the applications in runtime verification have been focused on the dynamic analysis of software, even though there are many more potential applications to other computational devices and target systems. In this paper we present a collection of challenges for runtime verification extracted from concrete application domains, focusing on the difficulties that must be overcome to tackle these specific challenges. The computational models that characterize these domains require to devise new techniques beyond the current state of the art in runtime verification.