The concept of features provides an elegant way to specify families of systems. Given a base system, features encapsulate additional functionalities that can be activated or deactivated to enhance or restrict the base system’s behaviors. Features can also facilitate the analysis of families of systems by exploiting commonalities of the family members and performing an all-in-one analysis, where all systems of the family are analyzed at once on a single family model instead of one-by-one. Most prominent, the concept of features has been successfully applied to describe and analyze (software) product lines. We present the tool ProFeat that supports the feature-oriented engineering process for stochastic systems by probabilistic model checking. To describe families of stochastic systems, ProFeat extends models for the prominent probabilistic model checker Prism by feature-oriented concepts, including support for probabilistic product lines with dynamic feature switches, multi-features and feature attributes. ProFeat provides a compact symbolic representation of the analysis results for each family member obtained by Prism to support, e.g., model repair or refinement during feature-oriented development. By means of several case studies we show how ProFeat eases family-based quantitative analysis and compare one-by-one and all-in-one analysis approaches.

Modern business applications are getting increasingly distributed as multi-tenant software as a service (SaaS). This leads to new challenges in terms of quality assurance, because all customers are directly affected by software changes. The resulting problem is to proactively determinate evolutionary effects. Because SaaS applications are often realized in the sense of a software product line, this thesis examines ways of using feature models to face the mentioned problem. For this purpose, two approaches are analyzed: extended feature models with quality attributes annotated per feature and the analysis of structural aspects of feature models and corresponding concrete configurations. The presented attributed feature model approach measures the quality of concrete configurations to make configurations comparable according to specific quality goals. Criteria are elicited for when configurations can be compared to draw helpful conclusions. The structural approach focuses economic questions that are quality assurance related, such as identifying features that none of the tenants selected in their application configurations. Furthermore, three algorithms are presented that demonstrate the structural analysis approach to gather information relevant to quality assurance.