In software product line engineering, the design of assets for reuse and the derivation of software products entails low-level and highlevel decision making. In this process, two major types of decisions must be addressed: variability decisions, i.e., decisions made as part of variability management, and architectural decisions, i.e., fundamental decisions to be made during the design of the architecture of the product line or the products. In practice, variability decisions often overlap with or influence architectural decisions. For instance, resolving a variability may enable or prevent some architectural options. This inherent interdependence has not been explicitly and systematically targeted in the literature, and therefore, is mainly resolved in an ad hoc and informal manner today. In this paper, we discuss possible ways how variability and architectural decisions interact, as well as their management and integration in a systematic manner. We demonstrate the integration between the two types of decisions in a motivating case and leverage existing tools for implementing our proposal.

Many systems in the industrial automation domain include information systems. They manage manufacturing processes and control numerous distributed hardware and software components. In current practice, the development and reuse of such systems is costly and time-consuming, due to the variability of systems' topology and processes. Up to now, product line approaches for systematic modeling and management of variability have not been well established for such complex domains. In this paper, we present a model-based approach to support the derivation of systems in the target domain. The proposed architecture of the derivation infrastructure enables feature-, topology- and process configuration to be integrated into the multi-staged derivation process. We have developed a prototype to prove feasibility and improvement of derivation efficiency. We report the evaluation results that we collected through semi-structured interviews from domain stakeholders. The results show high potential to improve derivation efficiency by adopting the approach in practice. Finally, we report the lessons learned that raise the opportunities and challenges for future research

Software product line engineering helps organizations to achieve systematic software reuse by taking advantage of commonalities and predicted variability. Variability management has been considered as one important issue in product line development. In this paper, a variability analysis in production control systems reveals that the variability in such systems lays in the dynamic behavior and interaction of configured components, which we consider as behavior variability. This paper identifies the three main challenges to be solved for applying a product line approach to the domain of production control systems: (1) the selection or design of a proper variability language for describing the flexible behavior variability, (2) the need to model variability of behavior at different levels of granularity, as well as to map the elements among different levels, (3) the binding of behavioral variation points and variants into the various involved systems in a manageable way

The systems of industrial automation management (IAM) are in the domain of information systems. IAM systems have software components that support manufacturing processes. The operational parts of IAM coordinate highly plug compatible hardware devices. These functions of the IAM systems lead to process and topology variability, which result in development and reuse challenges for software engineers in practice. This paper presents an approach aiming at improving the development and derivation of one IAM software family within Siemens. The approach integrates feature modeling with domain-specific modeling languages (DSMLs) for variability representation. Moreover, by combining code generation techniques, the configuration of variability models can be used to automate the software derivation. We report a case study of applying the approach in practice. The outcome shows the enhancement of variability representation by introducing DSMLs and the improvement on automating software derivation. Finally, we report the lessons learned during the execution of this case study

Bok och Bibliotheksvasen, XXXVII (1950), 56-70, and was reprinted as the title essay of a collection by the author published in Copenhagen in 1952 by Ejnar Munksgaard (Library Research Monograph No. 3). The translator is indebted to Professor Karl J. Arndt of Clark University for invaluable assistance in making the translation. Permission to translate and print has been granted.-Albert C. Gerould, Free Library of Philadelphia.