Uwe Ryssel

Extraction of feature models from formal contexts

For economical reasons, the creation of feature oriented software should include previously created products and should not be done from scratch. To speed up this migration process, feature models have to be generated automatically from existing product variants. This work presents an approach based on formal concept analysis that analyzes incidence matrices containing matching relations as input and creates feature models as output. The resulting feature models describe exactly the given input variants. The introduced novel optimized approach performs this transformation in reasonable time even for large product libraries.

Automatic variation-point identification in function-block-based models

Function-block-based modeling is often used to develop embedded systems, particularly as system variants can be developed rapidly from existing modules. Generative approaches can simplify the handling and development of the resulting high variety of function-block-based models. But they often require the development of new generic models that do not utilize existing ones. Reusing existing models will significantly decrease the effort to apply generative programming. This work introduces an automatic approach to recognize variants in a set of models and identify the variation points and their dependencies within variants. As result it offers automatically generated feature models and ICCL content to regenerate the given variants.

Efficient synthesis of feature models

Abstract Context Variability modeling, and in particular feature modeling, is a central element of model-driven software product line architectures. Such architectures often emerge from legacy code, but, creating feature models from large, legacy systems is a long and arduous task. We describe three synthesis scenarios that can benefit from the algorithms in this paper. Objective This paper addresses the problem of automatic synthesis of feature models from propositional constraints. We show that the decision version of the problem is NP-hard. We designed two efficient algorithms for synthesis of feature models from CNF and DNF formulas respectively. Method We performed an experimental evaluation of the algorithms against a binary decision diagram (BDD)-based approach and a formal concept analysis (FCA)-based approach using models derived from realistic models. Results Our evaluation shows a 10 to 1,000-fold performance improvement for our algorithms over the BDD-based approach. The performance of the DNF-based algorithm was similar to the FCA-based approach, with advantages for both techniques. We identified input properties that affect the runtimes of the CNF- and DNF-based algorithms. Conclusions Our algorithms are the first known techniques that are efficient enough to be used on dependencies extracted from real systems, opening new possibilities of creating reverse engineering and model management tools for variability models.

Automatic library migration for the generation of hardware-in-the-loop models

Embedded systems are widely used in several applications nowadays. As they integrate hard- and software elements, their functionality and reliability are often tested by hardware-in-the-loop methods, in which the system under test runs in a simulated environment. Due to the rising complexity of the embedded functions, performance limitations and practicability reasons, the simulations are often specialized to test specific aspects of the embedded system and develop a high diversity by themselves. This diversity is difficult to manage for a user and results in erroneously selected test components and compatibility problems in the test configuration. This paper presents a generative programming approach that handles the diversity of test libraries. Compatibility issues are explicitly evaluated by a new interface concept. Furthermore, a novel model analyzer facilitates the efficient application in practice by migrating existing libraries. The approach is evaluated for an example from the automotive domain using MATLAB/Simulink.

Fast algorithms for implication bases and attribute exploration using proper premises

A central task in formal concept analysis is the enumeration of a small base for the implications that hold in a formal context. The usual stem base algorithms have been proven to be costly in terms of runtime. Proper premises are an alternative to the stem base. We present a new algorithm for the fast computation of proper premises. It is based on a known link between proper premises and minimal hypergraph transversals. Two further improvements are made, which reduce the number of proper premises that are obtained multiple times and redundancies within the set of proper premises. We have evaluated our algorithms within an application related to refactoring of model variants. In this application an implicational base needs to be computed, and runtime is more crucial than minimal cardinality. In addition to the empirical tests, we provide heuristic evidence that an approach based on proper premises will also be beneficial for other applications. Finally, we show how our algorithms can be extended to an exploration algorithm that is based on proper premises.

Generation of function block based designs using Semantic Web technologies

Embedded systems are used in many domains today. However, the design process of these systems is complex and time-consuming. In automation domains the complexity can be decreased by using function block based designs to specify the functionality of the system to be created. But this has to be done still manually. This paper introduces an approach to generate such function block based designs automatically from requirements. Therefore a generative approach is used, where design patterns were assembled to complete designs. The pattern description and the generation process are realized by Semantic Web technologies. The evaluation of the approach is exemplified by functional schematics of room automation designs.

Generative function block design and composition

The design of large and complex distributed automation systems bases nowadays in many aspects on function blocks and prefabricated devices. These modular concepts of reuse probably reduce the design time and other automation composition concepts have to improve the speed further. But nearly each project contains special purpose applications, which still require a lot of time to develop new solution or adopt existing ones. This paper introduces a design concept spanning both worlds to take advantage of automatic function block composition and the fast design of special purpose devices. It uses a generative method and generic libraries specially adopted to the interoperability requirements of automation systems.

Reasoning of feature models from derived features

When using product lines, whose variability models are based on derived features, e.g., Simulink variant objects, the dependencies among the features are only described implicitly. This makes it difficult to verify the mapping of the features to the solution space and to create a comprehensive overview of the feature dependencies like in a feature model. In this paper, an OWL-based approach is presented, which permits the automatic verification of the feature mapping and an automatic feature model synthesis for derived features using OWL reasoning and formal concept analysis.

Holistic design of wireless building automation systems

Wireless building automation systems are gaining momentum as they promise an easy installation in old and new buildings. But, the design of wireless building automation systems is still an extensive manual process with little to no tool support. In result, the system commissioning ends in trial-and-error set-ups to identify interoperable devices, to solve issues with wireless signal propagation and to understand energy problems of nodes. This paper introduces a holistic design approach that addresses these common issues in wireless building automation system design. It takes up novel design concepts and tools from wired system design and combines them in a holistic tool environment that supports the engineer in his common work flow to efficiently design reliable wireless building automation systems.

Multi-objective combinatorial optimization for designing room automation systems by using evolutionary algorithms

The domain of building automation consists of system designs with huge complexity, whereas the major part is the room automation designs. These designs are realized by prefabricated devices which are offered by various manufacturers that support a broad spectrum of functions. This results in the problem of choosing a combination of devices which optimally realize the requirements. In this paper, a multi-objective combinatorial optimization method is presented including test results that can automatically generate optimal multi-vendor system designs by using an evolutionary algorithm with problem-specific operations.