Incorrect cryptographic protocol implementation and malware attacks targeting its runtime may lead to insecure execution even if the protocol design has been proven safe. This research focuses on adapting a runtime-verification-centric trusted execution environment (RV-TEE) solution to a cryptographic protocol deployment -particularly that of the Secure Shell Protocol (SSH). We aim to show that through a concrete realization of RV-TEE, which is neither tied to specific CPU mode nor requires the consequential operating system support, SSH execution can be rendered trustworthy. We provide: (i) An RV-TEE setup for a popular SSH implementation based on a widely-adopted RV tool, and a USB-connected hardware security module (ii) An overview of the property extraction process through a methodical analysis of the SSH protocol specifications (iii) Security vulnerabilities identified as a result of RV-TEE adoption (iv) An overhead analysis delineating what SSH applications can benefit from our proposed setup in a practical manner.

Software Engineering principles and practices promote the reuse of existing elements to maximize the performance of development and maintenance processes.The core concepts of reusability are aligned with the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles.From our perspective, FAIR principles are not only related to data in a software system, because there are other resources and parts of a system that are related to data indirectly or require data schemas and models for their description and management.Examples of these resources are services, business logic processes, contracts, security policies, architectures, components, deployment resources, execution rules, or interaction alternatives, among other possible examples.Actually, any resource in a system may need to be discovered either internally by a subprocess, or externally by any client that might be interested in finding, accessing, interoperating, or reusing it.This paper addresses a systematic literature review to analyze the current principles, paradigms, and technologies that can be applied to enable discovering and reusing mechanisms in modern software systems, and how traditional and outdated approaches have been adapted to support present issues.From this analysis, we outlined the challenges and open research lines that can be addressed to achieve a suitable reuse strategy.

The modularization of domain-specific modeling languages (DSMLs) fosters individual reuse of DSMLs in different contexts.Within this article, we discuss how it is possible to refer to model elements of other languages when composing different DSMLs.Related approaches usually rely on a DSML-agnostic language infrastructure that tests the compatibility of model elements via types encoded in Strings without any consistency checks.We propose the "strongly kind-typed" symbol table as an extension to the compiler approach to integrate the syntax of the languages using symbol tables that assign a symbol kind to each name definition.Our approach can be integrated into language workbenches that provide a symbol table infrastructure as part of a DSML implementation.The kind-typed symbol tables are integrated into the language workbench MontiCore.Strongly kind-typed symbol tables utilize the type system of the language workbench's host language to ensure type consistency between the language-specific symbol table infrastructures during DSML composition, which ultimately supports DSML engineering in the large.

Verifying whether a UML class diagram is consistent involves finding valid instances that provably meet its constraints defined in Object Constraint Language (OCL).Recent studies have shown that many existing tools and techniques not only can find valid instances but also pinpoint the conflicts among the OCL constraints.However, they do not scale well and are often unable to locate the conflicts when the number of OCL constraints significantly increases.In this paper, we present a novel approach that is capable of verifying UML class diagrams with a large number of OCL constraints.Our approach has two distinct features: (1) it provides a query language that allows users to choose parts of a UML class diagram to be verified.( 2) a new algorithm that can handle an extreme size of OCL invariants via concurrent verification.We have implemented a new automated tool called: QMaxUSE.The evaluation results suggest that QMaxUSE has the potential to be adapted by industry and offers up to 30x efficiency improvement in verifying UML class diagrams with a large number of OCL constraints.

Complex Event Processing (CEP) is a powerful technology for analyzing and correlating large amounts of data coming from different application domains to automatically detect situations of interest (event patterns) in real time. However, extensive knowledge on CEP is required to be able to implement CEP applications. To alleviate this situation, in recent years, several works have proposed the use of Model-Driven Engineering (MDE) to facilitate the development of such CEP applications for domain experts. In this paper, we propose a systematic literature review of existing approaches, frameworks, systems and languages that integrate MDE with CEP, along with the application domains and maturity levels with which these proposals have been successfully adopted. Based on our findings, future research challenges in the CEP field are also discussed.

To understand and manipulate a complex system, it is necessary to apply the separation of concerns and produce separate models, called viewpoints models. These models represent views on the system that correspond to distinct business domains. They are generally heterogeneous, i.e. conform to different meta-models. The management of the system's global model (a complete view of the system) requires the identification of the existing correspondences among the viewpoints models.

Many modeling languages share common concepts, such as types, expressions, statements, or literals. Nonetheless, these essential language concerns are often developed again and again, independently, and with minor changes here and there. A well-designed and extensible library of such, tried-and-tested, language components can facilitate engineering new languages through their reuse. We utilize the powerful language composition techniques of the MontiCore language workbench to conceive such a library, present its components, and their rationale. This fosters language engineering in MontiCore and can inspire more efficient language engineering in the technological spaces of other language workbenches.

Model-Driven Engineering (MDE) is a discipline that leverages abstraction and automation in software development. Projects are typically composed of inherently different artifacts, including models, metamodels, model transformations, code generators, and concrete syntax definitions. Despite the increasing availability of reusable projects (e.g., through GitHub), their reuse possibilities depend on the availability of accurate, high-level metadata describing architectural information about the project at hand. In this paper, we enhance an existing approach for extracting relevant architectural information from model-driven projects to detect subsequent metamodel versions in evolution paths. In particular, we are interested in those refactorings that enhance the intrinsic quality of metamodels. The approach has been implemented by extending the existing MDEprofiler tool and has been validated on a dataset consisting of metamodels with different size and covering distinct application domains.

Graph transformation units are rule-based devices to specify processes on graphs and the dynamics of informationprocessing systems with graphs as states. In this article, we propose graph transformation units as a framework to model graph algorithms, to prove their correctness and to analyze their complexity. A specific emphasis is laid on the improvement of the efficiency of graph algorithms by massive parallelism.

The research leading to these results has received funding from the European Commission’s H2020 Programme under grant agreement numbers 780351 (ENACT).