Generative programming: methods, tools, and applications
Generative programming: methods, tools, and applications
Feature interaction: a critical review and considered forecast
Computer Networks: The International Journal of Computer and Telecommunications Networking
Software Fault Interactions and Implications for Software Testing
IEEE Transactions on Software Engineering
Software Product Line Engineering: Foundations, Principles and Techniques
Software Product Line Engineering: Foundations, Principles and Techniques
IPOG: A General Strategy for T-Way Software Testing
ECBS '07 Proceedings of the 14th Annual IEEE International Conference and Workshops on the Engineering of Computer-Based Systems
IEEE Transactions on Software Engineering
Feature Interaction Aware Test Case Generation for Embedded Control Systems
Electronic Notes in Theoretical Computer Science (ENTCS)
An algorithm for generating t-wise covering arrays from large feature models
Proceedings of the 16th International Software Product Line Conference - Volume 1
Incremental model-based testing of delta-oriented software product lines
TAP'12 Proceedings of the 6th international conference on Tests and Proofs
Generating better partial covering arrays by modeling weights on sub-product lines
MODELS'12 Proceedings of the 15th international conference on Model Driven Engineering Languages and Systems
Using feature model knowledge to speed up the generation of covering arrays
Proceedings of the Seventh International Workshop on Variability Modelling of Software-intensive Systems
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Testing software systems plays a pivotal role for quality, reliability, and safety of such systems. Several approaches exist that provide efficient algorithms to test one software system. However, in the context of variable software systems, called software product lines (SPLs), testing has to deal with potentially thousands of variants. Unfortunately, current approaches do not scale to this problem and thus testing SPLs efficiently is a challenging task. In this paper, we propose an approach to reduce the test set by explicitly modeling information about shared resources and communication in feature models. As a result, we can figure out features that interact with each other and thus are more likely to cause problems. We show with a small case study that our approach reduces both, the features under test as well as the time for computing all feature combinations to be tested.