Reducing the Test Effort of Variability-Rich Systems by Using Feature Interaction Knowledge and Variability-Aware Source Code Analysis

To keep up with the growing demand for customized software solutions that are tailored to specific customer requirements, techniques like Software Product Line Engineering (SPLE) or the more ad-hoc clone-and-own (where engineers do not build each product from anew, but instead maximize the reuse of the available assets in building product families) have been devised. However testing such highly variable software systems has proven challenging. To improve this, the goal of this doctoral research plan is to propose an automated approach to reduce the effort for testing, while improving its effectiveness. The proposed approach aims to detect code parts that have to be considered when devising a test suite by employing source code analysis and test execution monitoring, which we argue have not been used to their full potential in this context.