Ina Schaefer: Part I: Future-Proof Software and Part II: How to test the universe? - Efficient Testing of Software Product Lines

Abstract
Part I: Modern software systems are extremely long-lived and evolve over time in order to adapt to changing user requirements, application contexts or technological changes. This leads to challenges for software design, implementation, quality assurance and re-engineering in order to make those software systems future-proof. In this talk, I will present an overview of ongoing research in my group at the Institute of Software Engineering and Automotive Informatics at TU Braunschweig addressing the challenges of long-living and evolving software systems. I will focus on two areas of our work: (1) applying machine learning techniques for efficient system-level regression testing and (2) similarity-based variability model mining in order to transform grown software systems into well-structured software product line.

Part II: Software product line engineering has gained considerable momentum in recent years, both in industry and in academia. A software product line is a family of software products that share a common set of features. Software product lines challenge traditional analysis, test and verification techniques in their quest of ensuring correctness and reliability of software. Simply creating and testing all products of a product line is usually not feasible, due to the potentially exponential number of valid feature combinations. In this talk, I present different strategies for software product line testing and focus on our recent work on incremental test case selection and test case prioritization of efficient product line testing.

Bio
Ina Schaefer is full professor and head of the Institute of Software Engineering and Automotive Informatics at Technische Universität Braunschweig, Germany. She received her PhD in 2008 at Technische Universität Kaiserslautern and was a Postdoc at Chalmers University, Gothenburg, Sweden. Her research interests include constructive and analytic approaches for developing correct software systems, with a particular focus on software variability and evolution, as well as re-engineering techniques for legacy software systems.