An introduction to Graph Data Management

A graph database is a database where the data structures for the schema and/or instances are modeled as a (labeled)(directed) graph or generalizations of it, and where querying is expressed by graph-oriented operations and type constructors. In this article we present the basic notions of graph databases, give an historical overview of its main development, and study the main current systems that implement them

Towards Practical Graph-Based Verification for an Object-Oriented Concurrency Model

To harness the power of multi-core and distributed platforms, and to make the development of concurrent software more accessible to software engineers, different object-oriented concurrency models such as SCOOP have been proposed. Despite the practical importance of analysing SCOOP programs, there are currently no general verification approaches that operate directly on program code without additional annotations. One reason for this is the multitude of partially conflicting semantic formalisations for SCOOP (either in theory or by-implementation). Here, we propose a simple graph transformation system (GTS) based run-time semantics for SCOOP that grasps the most common features of all known semantics of the language. This run-time model is implemented in the state-of-the-art GTS tool GROOVE, which allows us to simulate, analyse, and verify a subset of SCOOP programs with respect to deadlocks and other behavioural properties. Besides proposing the first approach to verify SCOOP programs by automatic translation to GTS, we also highlight our experiences of applying GTS (and especially GROOVE) for specifying semantics in the form of a run-time model, which should be transferable to GTS models for other concurrent languages and libraries.Comment: In Proceedings GaM 2015, arXiv:1504.0244

Evaluation Criteria for Object-oriented Metrics

In this paper an evaluation model for object-oriented (OO) metrics is proposed. We have evaluated the existing evaluation criteria for OO metrics, and based on the observations, a model is proposed which tries to cover most of the features for the evaluation of OO metrics. The model is validated by applying it to existing OO metrics. In contrast to the other existing criteria, the proposed model is simple in implementation and includes the practical and important aspects of evaluation; hence it suitable to evaluate and validate any OO complexity metric

Dynamically typed languages

Dynamically typed languages such as Python and Ruby have experienced a rapid grown in popularity in recent times. However, there is much confusion as to what makes these languages interesting relative to statically typed languages, and little knowledge of their rich history. In this chapter I explore the general topic of dynamically typed languages, how they differ from statically typed languages, their history, and their defining features

Metamodel-based model conformance and multiview consistency checking

Model-driven development, using languages such as UML and BON, often makes use of multiple diagrams (e.g., class and sequence diagrams) when modeling systems. These diagrams, presenting different views of a system of interest, may be inconsistent. A metamodel provides a unifying framework in which to ensure and check consistency, while at the same time providing the means to distinguish between valid and invalid models, that is, conformance. Two formal specifications of the metamodel for an object-oriented modeling language are presented, and it is shown how to use these specifications for model conformance and multiview consistency checking. Comparisons are made in terms of completeness and the level of automation each provide for checking multiview consistency and model conformance. The lessons learned from applying formal techniques to the problems of metamodeling, model conformance, and multiview consistency checking are summarized