Artificial intelligence has already proven to be a powerful tool to automate and improve how
we deal with software development processes. The application of artificial intelligence to …

Usage-based statistical testing employs knowledge about the actual or anticipated usage
profile of the system under test for estimating system reliability. For many systems, usage …

Version control of large-scale models is still an open problem in Model Driven Engineering
settings. In this paper we review a number of existing approaches for model version control …

One of the main challenges in model-driven software engineering is to automate the
resolution of design model inconsistencies. We propose to use the artificial intelligence …

The increasing adoption of MDE (Model Driven Engineering) favored the use of large
models of different types. It turns out that when the modeled system gets larger, simply …

Knowledge Graph (KG) usually contains billions of facts about the real world, where a fact is
represented as a triplet in the form of (head entity, relation, tail entity). KG is a complex …

Automated model generation can be highly beneficial for various application scenarios
including software tool certification, validation of cyber-physical systems or benchmarking …

Fuzzing or fuzz testing is an established technique that aims to discover unexpected
program behavior (eg, bugs, security vulnerabilities, or crashes) by feeding automatically …

Graphs are ubiquitous in computer science. Moreover, in various application fields, graphs
are equipped with attributes to express additional information such as names of entities or …

Abstract Scalability in Model-Driven Engineering (MDE) is often a bottleneck for industrial
applications. Industrial scale models need to be persisted in a way that allows for their …