Compile-time variability is paramount in many software systems: Users can select desired features and generate a product tailored for their needs. For example, the Linux kernel has over 10000 such compile-time configuration options. Typically organized as software product lines, there are many different implementation mechanisms that can all be regarded as generators that produce products based on variability specifications.

However, variability often implies complexity. Already with few configuration options, we can generate a vast number (easily exceeding billions) of potential products. Checking all products individually using classic testing or analysis techniques is not feasible.

Recently, researchers have begun to adapt existing analysis techniques, such as type checking, model checking, theorem proving, static analysis, and parsing, to incorporate variability. Usually, the idea is to check the entire product line in a single step and to reason about variability locally where it manifests at the code level. When the product line passes the check (e.g., the product line is well-typed), it is guaranteed that all possible generated products pass the check as well (e.g., all products that can be generated are well-typed, too). Such variability-aware analyses take variability information into account, including feature models and implementation artifacts, thus spanning problem and solution space.

In the talk, we provide an overview of concepts for variability-aware analysis, discuss different strategies (brute force, sampling, family-based analysis, feature-based analysis), and show recurring ideas and patterns of how existing analysis are extended for variability. While taking a broad view, we illustrate key ideas and also initial results by means of a number of case studies, including the development of a variability-aware type system and its application to Linux and the application of variability-aware verification techniques to product-line models.

Biography

Christian Kästner is a researcher in the Programming Languages Group at the Philipps University Marburg, Germany. He received his Ph.D. in Computer Science in 2010 from the University of Magdeburg, Germany for his work on virtual separation of concerns, which included developing a variability-aware type system for software product lines. His research focuses on correctness and understanding of systems with variability, including work on implementation mechanisms, tools, different kinds of analyses, feature interactions, and variability mining and refactoring. He is the author or coauthor of over a fifty peer-reviewed scientific publications.

Sven Apel is the leader of the Software Product-Line Group funded by the esteemed Emmy Noether Programme of the German Research Foundation (DFG). The group resides at the University of Passau, Germany. Dr.-Ing. Sven Apel received his Ph. D. in Computer Science in 2007 from the University of Magdeburg, Germany. His research interests include novel programming paradigms, software engineering and product lines, and formal and empirical methods. He is the author or coauthor of over a hundred peer-reviewed scientific publications. Sven Apel has been a program committee member of several highly ranked international conferences. His work received awards by the Ernst Denert Foundation and the Karin Witte Foundation