Arne Haber

First-class variability modeling in Matlab/Simulink

Modern cars exist in an vast number of variants. Thus, variability has to be dealt with in all phases of the development process, in particular during model-based development of software-intensive functionality using Matlab/Simulink. Currently, variability is often encoded within a functional model leading to so called 150%-models which easily become very complex and do not scale for larger product lines. To counter these problems, we propose a modular variability modeling approach for Matlab/Simulink based on the concept of delta modeling [8, 9, 24]. A functional variant is described by a delta encapsulating a set of modifications. A sequence of deltas can be applied to a core product to derive the desired variant. We present a prototypical implementation, which is integrated into Matlab/Simulink and offers graphical editing of delta models.

Delta-oriented architectural variability using MontiCore

Modeling of software architectures is a fundamental part of software development processes. Reuse of software components and early analysis of software topologies allow the reduction of development costs and increases software quality. Integrating variability modeling concepts into architecture description languages (ADLs) is essential for the development of diverse software systems with high demands on software quality. In this paper, we present the integration of delta modeling into the existing ADL MontiArc. Delta modeling is a language-independent variability modeling approach supporting proactive, reactive and extractive product line development. We show how Δ-MontiArc, a language for explicit modeling of architectural variability based on delta modeling, is implemented as domain-specific language (DSL) using the DSL development framework MontiCore. We also demonstrate how MontiCores language reuse mechanisms provide efficient means to derive an implementation of Δ-MontiArc tool implementation. We evaluate Δ-MontiArc by comparing it with annotative variability modeling.

Engineering delta modeling languages

Delta modeling is a modular, yet flexible approach to capture spatial and temporal variability by explicitly representing the differences between system variants or versions. The conceptual idea of delta modeling is language-independent. But, in order to apply delta modeling for a concrete language, so far, a delta language had to be manually developed on top of the base language leading to a large variety of heterogeneous language concepts. In this paper, we present a process that allows deriving a delta language from the grammar of a given base language. Our approach relies on an automatically generated language extension that can be manually adapted to meet domain-specific needs. We illustrate our approach using delta modeling on a textual variant of statecharts.

Evolving delta-oriented software product line architectures

Diversity is prevalent in modern software systems. Several system variants exist at the same time in order to adapt to changing user requirements. Additionally, software systems evolve over time in order to adjust to unanticipated changes in their application environment. In modern software development, software architecture modeling is an important means to deal with system complexity by architectural decomposition. This leads to the need of architectural description languages that can represent spatial and temporal variability. In this paper, we present delta modeling of software architectures as a uniform modeling formalism for architectural variability in space and in time. In order to avoid degeneration of the product line model under system evolution, we present refactoring techniques to maintain and improve the quality of the variability model. Using a running example from the automotive domain, we evaluate our approach by carrying out a case study that compares delta modeling with annotative variability modeling.

Integration of heterogeneous modeling languages via extensible and composable language components

Effective model-driven engineering of complex systems requires to appropriately describe different specific system aspects. To this end, efficient integration of different heterogeneous modeling languages is essential. Modeling language integaration is onerous and requires in-depth conceptual and technical knowledge and effort. Traditional modeling lanugage integration approches require language engineers to compose monolithic language aggregates for a specific task or project. Adapting these aggregates to different contexts requires vast effort and makes these hardly reusable. This contribution presents a method for the engineering of grammar-based language components that can be independently developed, are syntactically composable, and ultimately reusable. To this end, it introduces the concepts of language aggregation, language embedding, and language inheritance, as well as their realization in the language workbench MontiCore. The result is a generalizable, systematic, and efficient syntax-oriented composition of languages that allows the agile employment of modeling languages efficiently tailored for individual software projects.

Composition of Heterogeneous Modeling Languages

Model-driven engineering aims at managing the complexity of large software systems by describing their various aspects through dedicated models. This approach requires to employ different modeling languages that are tailored to specific system aspects, yet can be interpreted together to form a coherent description of the total system. Traditionally, implementations of such integrated languages have been monolithic language projects with little modularization and reuse of language parts.

Systematic Language Extension Mechanisms for the MontiArc Architecture Description Language

Architecture description languages (ADLs) combine the benefits of component-based software engineering and model-driven development. Extending an ADL to domain-specific requirements is a major challenge for its successful application. Most ADLs focus on fixed features and do not consider domain-specific language extension. ADLs focusing on extensibility focus on syntactic augmentation only and neither consider semantics, nor the ADL’s tooling. We present a systematic extension method for the MontiArc component and connector ADL that enables extending its syntax and infrastructure. The MontiArc ADL is built on top of the MontiCore workbench for compositional modeling languages and leverages its powerful language integration facilities. Based on these, we conceived systematic extension activities and present their application to customizing MontiArc for three different domains. This application of software language engineering to ADLs reduces effort for their extension and the presented method guides developers in applying it to their domain. This ultimately fosters the application of ADLs to real-world domain-specific challenges.

Systematic synthesis of delta modeling languages

Delta modeling is a modular, yet flexible approach to capture variability by explicitly representing differences between system variants or versions. The conceptual idea of delta modeling is language-independent. But, to apply delta modeling to a concrete language, either a generic transformation language has to be used or the corresponding delta language has to be manually developed for each considered base language. Generic languages and their tool support often lack readability and specific context condition checking, since they are unrelated to the base language. In this paper, we present a process that allows synthesizing a delta language from the grammar of a given base language. Our method relies on an automatically generated language extension that can be manually adapted to meet domain-specific needs. We illustrate our method using delta modeling on a textual variant of architecture diagrams. Furthermore, we evaluate our method using a comparative case study. This case study covers an architectural, a structural, and a behavioral language and compares the preexisting handwritten grammars to the generated grammars as well as the manually tailored grammars. This paper is an extension of Haber et al. (Proceedings of the 17th international software product line conference (SPLC’13), pp 22–31, 2013).