Modeling of Secure and Dependable Applications Based on a Repository of Patterns: The SEMCO Approach

International audienceThe requirement for higher quality and seamless development of systems is continuously increasing, even in domains traditionally not deeply involved in such issues. Security and Dependability (S&D) requirements are incorporated to an increasing number of systems. These newer restrictions make the development of those systems more complicated than conventional systems. In our work, we promote a new approach called SEMCO (System and software Engineering with Multi-COncerns) combining Model-Driven Engineering (MDE) with a model-based repository of S&D patterns to support the design and the analysis of pattern-based secure and dependable system and software architectures. The modeling framework to support the approach is based on a set of modeling languages, to specify security and dependability patterns, resources and a set of property models, and a set of model transformation rules to specify some of the analysis activities. As part of the assistance for the development of S&D applications, we have implemented a tool-chain based on the Eclipse platform to support the different activities around the repository, including the analysis activities. The proposed approach was evaluated through a case study from the railway domain

Engineering secure systems: Models, patterns and empirical validation

Several development approaches have been proposed to handle the growing complexity of software system design. The most popular methods use models as the main artifacts to construct and maintain. The desired role of such models is to facilitate, systematize and standardize the construction of software-based systems. In our work, we propose a model-driven engineering (MDE) methodological approach associated with a pattern-based approach to support the development of secure software systems. We address the idea of using patterns to describe solutions for security as recurring security problems in specific design contexts and present a well-proven generic scheme for their solutions. The proposed approach is based on metamodeling and model transformation techniques to define patterns at different levels of abstraction and generate different representations according to the target domain concerns, respectively. Moreover, we describe an operational architecture for development tools to support the approach. Finally, an empirical evaluation of the proposed approach is presented through a practical application to a use case in the metrology domain with strong security requirements, which is followed by a description of a survey performed among domain experts to better understand their perceptions regarding our approach

The Train Benchmark: cross-technology performance evaluation of continuous model queries

In model-driven development of safety-critical systems (like automotive, avionics or railways), well- formedness of models is repeatedly validated in order to detect design flaws as early as possible. In many indus- trial tools, validation rules are still often implemented by a large amount of imperative model traversal code which makes those rule implementations complicated and hard to maintain. Additionally, as models are rapidly increas- ing in size and complexity, efficient execution of validation rules is challenging for the currently available tools. Checking well-formedness constraints can be captured by declarative queries over graph models, while model update operations can be specified as model transformations. This paper presents a benchmark for systematically assessing the scalability of validating and revalidating well-formedness constraints over large graph models. The benchmark defines well-formedness validation scenarios in the railway domain: a metamodel, an instance model generator and a set of well- formedness constraints captured by queries, fault injection and repair operations (imitating the work of systems engi- neers by model transformations). The benchmark focuses on the performance of query evaluation, i.e. its execution time and memory consumption, with a particular empha- sis on reevaluation. We demonstrate that the benchmark can be adopted to various technologies and query engines, including modeling tools; relational, graph and semantic databases. The Train Benchmark is available as an open- source project with continuous builds from https://github. com/FTSRG/trainbenchmark

The state of adoption and the challenges of systematic variability management in industry

Handling large-scale software variability is still a challenge for many organizations. After decades of research on variability management concepts, many industrial organizations have introduced techniques known from research, but still lament that pure textbook approaches are not applicable or efficient. For instance, software product line engineering—an approach to systematically develop portfolios of products—is difficult to adopt given the high upfront investments; and even when adopted, organizations are challenged by evolving their complex product lines. Consequently, the research community now mainly focuses on re-engineering and evolution techniques for product lines; yet, understanding the current state of adoption and the industrial challenges for organizations is necessary to conceive effective techniques. In this multiple-case study, we analyze the current adoption of variability management techniques in twelve medium- to large-scale industrial cases in domains such as automotive, aerospace or railway systems. We identify the current state of variability management, emphasizing the techniques and concepts they adopted. We elicit the needs and challenges expressed for these cases, triangulated with results from a literature review. We believe our results help to understand the current state of adoption and shed light on gaps to address in industrial practice.This work is supported by Vinnova Sweden, Fond Unique Interminist´eriel (FUI) France, and the Swedish Research Council. Open access funding provided by University of Gothenbur

Fundamental Approaches to Software Engineering

computer software maintenance; computer software selection and evaluation; formal logic; formal methods; formal specification; programming languages; semantics; software engineering; specifications; verificatio

A Bayesian learning approach to inconsistency identification in model-based systems engineering

Designing and developing complex engineering systems is a collaborative effort. In Model-Based Systems Engineering (MBSE), this collaboration is supported through the use of formal, computer-interpretable models, allowing stakeholders to address concerns using well-defined modeling languages. However, because concerns cannot be separated completely, implicit relationships and dependencies among the various models describing a system are unavoidable. Given that models are typically co-evolved and only weakly integrated, inconsistencies in the agglomeration of the information and knowledge encoded in the various models are frequently observed. The challenge is to identify such inconsistencies in an automated fashion. In this research, a probabilistic (Bayesian) approach to abductive reasoning about the existence of specific types of inconsistencies and, in the process, semantic overlaps (relationships and dependencies) in sets of heterogeneous models is presented. A prior belief about the manifestation of a particular type of inconsistency is updated with evidence, which is collected by extracting specific features from the models by means of pattern matching. Inference results are then utilized to improve future predictions by means of automated learning. The effectiveness and efficiency of the approach is evaluated through a theoretical complexity analysis of the underlying algorithms, and through application to a case study. Insights gained from the experiments conducted, as well as the results from a comparison to the state-of-the-art have demonstrated that the proposed method is a significant improvement over the status quo of inconsistency identification in MBSE.Ph.D