High resource demand for system testing is a major obstacle for continuous delivery. This resource demand can be reduced by prioritizing test cases, e.g., by focusing on tests that cover a lot of functionality. For large-scale systems, like an enterprise SOA, defining such test cases can be difficult for the tester because of the lack of relevant knowledge about the system. We propose an approach for test case prioritization and selection that is based on architectural viewpoint that provides software testers with the required architectural information. We outline how architectural information is used for defining and selecting prioritized test cases. The approach has been developed in close cooperation with the provider of an enterprise SOA in the banking domain in Austria following an action research approach. In addition, the approach has been validated in an industrial case study. Validation showed that there is no further need for manual architectural analysis to be able to prioritize and select test cases. We also show the limitations of our approach as it is based on static code analysis.

Industrial product lines are typically maintained for a long time and evolve continuously to address changing requirements and new technologies. Already derived products often have to be re-derived after such changes to benefit from new and updated features. Product line engineers thus frequently need to analyze the impact of changes to variability models to prevent unexpected changes of re-derived products. In this paper we present a tool-supported approach that informs engineers about the impacts of variability model changes on existing products. Regression tests are used to determine whether existing product configurations and generated product outputs can be re-derived without unexpected effects. We evaluate the feasibility of the approach based on changes observed in a real-world software product line. More specifically, we show how our approach helps engineers performing specific evolution tasks to analyze the change impacts on existing products. We also evaluate the performance and scalability of our approach. Our results show that variability change impact analyses can be automated using model regression testing and can help reducing the gap between domain engineering and application engineering.

The complexity of product line variability models makes it hard to maintain their consistency over time regardless of the modeling approach used. Engineers thus need support for detecting and resolving inconsistencies. We describe experiences of applying a tool-supported approach for incremental consistency checking on variability models. Our approach significantly improves the overall performance and scalability compared to batch-oriented techniques and allows providing immediate feedback to modelers. It is extensible as new consistency constraints can easily be added. Furthermore, the approach is flexible as it is not limited to variability models and it also checks the consistency of the models with the underlying code base of the product line. We report the results of a thorough evaluation based on real-world product line models and discuss lessons learned.